Urban Vehicle Tracking using a Combined 3D Model Detector and Classifier

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "Urban Vehicle Tracking using a Combined 3D Model Detector and Classifier"

Transcription

1 Urban Vehicle Tracing using a Combined 3D Model Detector and Classifier Norbert Buch, Fei Yin, James Orwell, Dimitrios Maris and Sergio A. Velastin Digital Imaging Research Centre, Kingston University, Penrhyn Road, Kingston upon Thames, KT1 2EE, United Kingdom {norbert.buch, fei.yin, j.orwell, d.maris, Abstract. This paper presents a tracing system for vehicles in urban traffic scenes. The tas of automatic video analysis for existing CCTV infrastructure is of increasing interest due to benefits of behaviour analysis for traffic control. Based on 3D wire frame models, we use a combined detector and classifier to locate ground plane positions of vehicles. The proposed system uses a Kalman filter with variable sample time to trac vehicles on the ground plane. The classification results are used in the data association of the tracer to improve consistency and noise suppression. Quantitative and qualitative evaluation is provided using videos of the public benchmaring i-lids data set provided by the UK Home Office. Correctly detected tracs of 94% outperform a baseline motion tracer tested under the same conditions. Keywords: vehicle tracing, visual surveillance, motion estimation, 3D models, vehicle classification, urban traffic 1 Introduction In recent years, there has been an increased scope for automatic analysis of urban traffic activity. This is due in part to the additional numbers of cameras and other sensors, the enhanced infrastructure and consequent accessibility and also the advancement of analytical techniques to process the video data. Monitoring objectives include the detection of traffic violations (illegal turns, one way streets, etc.) and the gathering of statistics about the type of road users. Using general purpose surveillance cameras, the classification of vehicles is a demanding challenge (see [9, 8, 12, 4]). Compared to most examples in image retrieval problem, the quality of surveillance data is generally poor and the range of operational conditions (night-time, inclement and changeable weather that affects the auto-iris) require robust techniques which need to be immune to errors in obtaining road users silhouettes. Those silhouettes extracted by foreground analysis are the input to our classifier. The classification process is based on 3D models for vehicles to give robustness against foreground noise and can be restricted to an active region of the camera view (e.g. lanes). This allows human operators to configure monitoring objectives. The classified vehicles are traced on the ground plane over time using a Kalman filter for variable time steps. Tracing performance is evaluated using the framewor of Yin et al. [13] and compared to a state of the art OpenCV blob tracer [11] operating on the same video data

2 Our novel contributions are firstly the extension of our 3D vehicle detector and classifier by tracing on the ground plane. We derive a variable sample rate Kalman filter to accommodate missed observations. The classification of vehicles is used during tracing due to our novel approach of classifying before tracing. Secondly, our tracing evaluation framewor [13] is used to generate rich performance figures based on ground truth containing image bounding boxes. Thirdly, the performance of the 3D model based ground plane tracer is compared to a state of the art blob tracer. The remainder of the paper is organised as follows: Section 2 introduces the detector and classifier used. The application of Kalman filtering to the classification results is demonstrated in section 3. Introduction to the evaluation framewor and results are given in section 4. Section 5 concludes the paper. 1.1 Related wor This review firstly introduces detection and tracing systems and continues with performance evaluation framewors. Vehicle tracing in urban environments is performed in [12]. However, only a single 3D model for cars is used to estimate a vehicle constellation per frame with optimisation solved with a Marov Chain Monte Carlo (MCMC) algorithm. The reported detection rates are 96.8% and 88% for two videos, which are limited to single size vehicles. The paper of Morris and Trivedi [9] presents a combined tracing and classification approach for side views of highways which is an extension to [8]. A single Gaussian bacground model is used for foreground segmentation. Classification and tracing accuracy was increased by combining tracing and classification. A Kalman filter is used to trac the foreground regions based on the centroids in the image plane only. The OpenCV blob tracer [11] used as baseline here wors in a similar fashion. The field of generic object recognition recently expanded towards surveillance applications. Good examples are Leibe et al. [6,7] for vehicle and pedestrian detection. Performance however, is not yet comparable to state of the art surveillance systems for this specific tas. Performance evaluation has played an important role on developing, assessing and comparing object tracing algorithms. Lazarevic-McManus et al [5] evaluated performance of motion detection based on ROC-lie curves and the F-measure. The latter allows comparison using a single value domain, but is mainly designed to operate on motion detection rather than tracing. There is a significant body of wor dealing with evaluation of both motion detection and tracing. Needham and Boyle [1] proposed a set of metrics and statistics for comparing trajectories to account for detection lag, or constant spatial shift. However, taing only the trajectory (a set of points over time) as the input of evaluation may not give sufficient information about how precise the tracs are since the size of the object is not considered. Bashir and Porili [1] use the spatial overlap of ground truth and system bounding boxes which is not biased towards large objects. However they are counted per frame, which is justified when the objective is object detection. In object tracing, counting true positive (TP), false positive (FP) and false negative (FN) tracs is a more natural choice which is consistent with the expectations of surveillance end-users. Brown et al. [3] suggests a framewor for matching of system trac centroids and an enlarged ground truth bounding box which favours tracs of large objects

3 2 Detection and Classification using 3D Models Joint detection and classification is performed using 3D wire frame models for vehicles with calibrated cameras. As indicated in the bloc diagram in Figure 1, the detector uses a Gaussian Mixture Model (GMM) for motion estimation with subsequent closed contour retrieval to generate motion silhouettes for an input video frame. Those motion silhouettes are used to generate vehicle hypotheses. The classifier matches 3D wire frame models (see ) with the motion silhouettes. To validate the hypotheses, the normalised overlap area of motion silhouettes and projected model silhouettes is calculated. Full details on the classifier can be found in a previous paper [4]. The output of the classifier are class labelled ground plane positions of vehicles. On frame to frame detection and classification of four classes, the classifier precision is 96.1% with a total system recall of 9.4% at a precision of 87.9%. Section 5 gives tracing evaluation results on the same video set. Detector frame GMM Tracer foreground mas Classifier Closed Contours silhouettes Overlap Area [4] scores Maximum labels GP pos Kalman Filter tracs 3D Hypothesi s model silhouette GP positions 2D Projection] Models Figure 1 Bloc diagram of detector with 3D classifier and subsequent tracer. Figure 2 Left: 3D wire frame models used for the classifier. Right: Example of detection and classification with ground plane tracing. The wire frame projection in red is used to estimate the bounding box for traced vehicles

4 3 Tracing Tracing introduces temporal consistency to the detection and classification result of the previous section. Our novel contribution is the extension of the classifier by a Kalman filter with variable sample rate. The detector with joint classifier may reject valid vehicles in some frames due to noise, which requires the Kalman filter to operate on variable time intervals. Tracing is performed on the ground plane of the scene, which simplifies behaviour analysis lie bus lane monitoring. We use the standard formulation of the Kalman filter for a constant velocity model of vehicles x = Fx 1 + Bu + w z = Hx + v with u = (1) T with state vector = vx, x, vy, y z = x, y. All time and speed related constants for the filter are based on seconds rather than the sample rate or frame rate. The ground plane coordinates are in metres, all noise and position estimates are in metres or meters per second. The above is valid, if the integration constant T from speed to position in the transition matrix F is defined in seconds 1 T 1 F =. (2) 1 T 1 x and the measurement vector [ ] T The only conditions to operate the Kalman filter at variable sample rate is to update T in the transition matrix F constantly. For prediction steps, T is the time between the last update step of the filter and the current time. The state prediction xˆ and the error covariance P is therefore estimated for the correct time. If a measurement is available, the update step is performed with the same transition matrix F. If no measurement is available, not update is performed. Future prediction steps will be performed with increasing time T until an update taes place. Tracs can be discarded if the predicted error covariance P grows beyond a threshold. The parameters for the filter are as follows. The process noise w is set to 1.1m s for velocity and.7m for position. Those values can be derived from the expected acceleration of vehicles. The measurement noise is v = 2m corresponding to the detection grid. The initial error covariance P is set to 3m s for velocity and 1m for position. The initial position state corresponds to the detection position with zero velocity. The velocity is updated during the second detection using the first motion vector. Observations mi, are associated with tracs based on the distance d ij between the observation m i, and the prediction x ˆ j, normalised by the diagonal elements of the predicted error covariance P. Changes in the model- id of the last observation of a trac id i and the current observation id j are penalised. The total number of model- ids is 1. This novel approach is possible due to our system having classification before the tracing. ( ) ( ) ij = i j x + i j y + 1 i j d x x P y y P id id (3) - 4 -

5 4 Evaluation The object tracing performance is demonstrated by comparing our tracer with a baseline tracer (OpenCV blob tracer [11]). The OpenCV tracer uses an adaptive mixture of Gaussians for bacground estimation, connected component analysis for data association and Kalman filtering for tracing blob position and size. We use the i-lids bench- maring video data set provided by the UK Home Office [2] for evaluation. We run the tracer on the following sequences of the pared car data set scene 1 (PVTRA1xxxx): 1a3, 1a7, 1a13, 1a19, 1a2, 2a5, 2a1 and 2a11. Those videos contain overcast, sunny, changing weather conditions and camera saturation. We propose a rich set of metrics such as Correct Detected Tracs, False Detected Tracs and Trac Detection Failure to provide a general overview of the system s performance. Trac Fragmentation shows whether the temporal and spatial coherence of tracs is established. ID Change is useful to test the data association module of the system. Latency indicates how quic the system can respond to an object entering the camera view, and Trac Completeness how complete the object has been traced. Metrics such as Trac Distance Error and Closeness of Tracs indicate the accuracy of estimating the position, the spatial and the temporal extent of the objects respectively. More details about this evaluation framewor can be found in Yin et al. [13]. 4.1 Qualitative results Figure 3 Correct detected tracs inside the active regions of interest (dar red boxes). Left: the proposed system with corresponding ground plane tracs. Right: OpenCV tracer result

6 Figure 4 The second car is missed due to occlusion between the vehicles. The proposed classifier on the left correctly locates the first car. The OpenCV tracer merged both cars with a large bounding box at a central position. Figure 5 Pedestrians are correctly rejected as other class by the proposed classifier and detected by the OpenCV tracer. 4.2 Quantitative results The ground truth used for evaluation is provided with the i-lids data set. It is of limited duration within the videos and does not include pedestrians on the road. The evaluation was constrained to the two regions of interest on the road (dar red boxes in Figure 3) for both tracers. The full results are provided in Table 1 indicating that the proposed system outperforms the OpenCV tracer on high level metrics such as correct detected tracs, trac detection failure, false detected tracs and trac fragmentation. This can mainly be attributed to the additional prior information from using 3D models to classify the content of the input video. For metrics that evaluate the motion segmentation such as trac closeness and distance error, both tracers have similar performance, which can be explained by the similar bacground estimation method. The trac closeness of the proposed system is better than the baseline due to 3D models which are more robust against shadows, which can be observed for the bus in Figure 3 and the occluded car in Figure 4. The extent of the projected wire frame model is used as bounding box for the proposed system. The false detected tracs from the OpenCV tracer are high due to systematic detection of pedestrians, which can not be classified. Refer to Figure 5 for an example. The proposed system detected 94% of the ground truth tracs compared to - 6 -

7 Table 1 Tracing results Metrics proposed Tracer OpenCV blob Tr. Number of Ground truth tracs 1 1 Number of system tracs Correct detected tracs Trac detection failure 6 12 False detected tracs 27 9 Latency [frames] 5 5 Trac fragmentation 8 18 Average trac Completeness [time] 64% 55% ID change 1 3 Average trac closeness [bbox overlap] 54% 35% Standard Deviation of closeness 2% 13% Average distance error [pixels] Standard Deviation of distance error % of the base line. Our system has half of the trac detection failures compared to the base line. The higher detection rate can be explained by a more sensitive bacground estimation producing more complete and additional noise detections. However, the classification stage rejects many ambiguous detections. Id change can occurs if a trac of an object leaving is continued for a new object. This is worse for the proposed system compared to the OpenCV tracer, because the tracer is more persistent, occasionally wrongly continuing a trac but therefore generating much less trac fragmentations. 5 Conclusions and future wor We proposed a novel system for detection, classification and ground plane tracing of vehicles in surveillance videos. The proposed system is evaluated on the i-lids data set against the state of the art OpenCV blob tracer. Our system performs similar for motion related metric but outperforms the baseline for high level metric lie detected tracs 94% and missed tracs 6. This indicates superior performance in the camera view with the additional benefit of gaining group plane locations. This can be essential to solve surveillance tass lie enforcing bus lane restrictions. Future wor can be the evaluation of the classes of tracs and the group plane positions. Both require a significant amount of ground truth. Regarding the detector and classifier, avoiding the reliance on motion estimation would be beneficial for more robustness against lighting changes and camera saturation. There is the opportunity to post process completed tracs for retrospective behaviour analysis

8 6 Acnowledgements We are grateful to the Directorate of Traffic Operations at Transport for London for funding the wor on classification and tracing and to BARCO View, Belgium for funding the wor on tracing evaluation. 7 References [1] F. Bashir and F. Porili. Performance evaluation of object detection and tracing systems. In IEEE Int. W. on Performance Evaluation of Tracing and Surveillance, PETS'6, 26. [2] Home Office Scientific Development Branch. Imagery library for intelligent detection systems i-lids. [accessed 19 December 28]. [3] L. M. Brown, A. W. Senior, Ying li Tian, Jonathan Connell, Arun Hampapur, Chiao-Fe Shu, Hans Merl, and Max Lu. Performance evaluation of surveillance systems under varying conditions. In IEEE Int'l Worshop on Performance Evaluation of Tracing and Surveillance, pages 1 8. Colorado, January 25. [4] Norbert Buch, James Orwell, and Sergio A. Velastin. Detection and classification of vehicles for urban traffic scenes. In International Conference on Visual Information Engineering VIE8, pages IET, July 28. [5] N. Lazarevic-McManus, J.R. Renno, D. Maris, and G.A. Jones. An object-based comparative methodology for motion detection based on the f-measure. Computer Vision and Image Understanding, Sp. Is. on Intelligent Visual Surveillance, pages 74 85, 27. [6] B. Leibe, N. Cornelis, K. Cornelis, and L. Van Gool. Dynamic 3d scene analysis from a moving vehicle. In Computer Vision and Pattern Recognition. CVPR '7. IEEE Conference on, pages 1 8, June 27. [7] B. Leibe, K. Schindler, N. Cornelis, and L. Van Gool. Coupled object detection and tracing from static cameras and moving vehicles. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 3(1): , Oct. 28. [8] B. Morris and M. Trivedi. Robust classification and tracing of vehicles in traffic video streams. In Intelligent Transportation Systems Conference. ITSC '6. IEEE, pages , 26. [9] Brendan Morris and Mohan Trivedi. Improved vehicle classification in long traffic video by cooperating tracer and classifier modules. In AVSS '6: Proceedings of the IEEE International Conference on Video and Signal Based Surveillance, page 9, USA, 26. [1] C.J. Needham and R.D. Boyle. Performance evaluation metrics and statistics for positional tracer evaluation. In International Conference on Computer Vision Systems, ICVS'3, pages , Graz, Austria, April 23. [11] OpenCV. Open source computer vision library. [accessed 19 December 28]. [12] Xuefeng Song and R. Nevatia. Detection and tracing of moving vehicles in crowded scenes. In Motion and Video Computing. WMVC '7. IEEE W. on, pages 4 4, 27. [13] Fei Yin, Dimitrios Maris, and Sergio A. Velastin. Performance evaluation of object tracing algorithms. In 1th IEEE International Worshop on Performance Evaluation of Tracing and Surveillance, PETS'7, Rio de Janeiro, October

VEHICLE LOCALISATION AND CLASSIFICATION IN URBAN CCTV STREAMS

VEHICLE LOCALISATION AND CLASSIFICATION IN URBAN CCTV STREAMS VEHICLE LOCALISATION AND CLASSIFICATION IN URBAN CCTV STREAMS Norbert Buch 1, Mark Cracknell 2, James Orwell 1 and Sergio A. Velastin 1 1. Kingston University, Penrhyn Road, Kingston upon Thames, KT1 2EE,

More information

Vision based Vehicle Tracking using a high angle camera

Vision based Vehicle Tracking using a high angle camera Vision based Vehicle Tracking using a high angle camera Raúl Ignacio Ramos García Dule Shu gramos@clemson.edu dshu@clemson.edu Abstract A vehicle tracking and grouping algorithm is presented in this work

More information

Tracking performance evaluation on PETS 2015 Challenge datasets

Tracking performance evaluation on PETS 2015 Challenge datasets Tracking performance evaluation on PETS 2015 Challenge datasets Tahir Nawaz, Jonathan Boyle, Longzhen Li and James Ferryman Computational Vision Group, School of Systems Engineering University of Reading,

More information

A Model-based Vehicle Segmentation Method for Tracking

A Model-based Vehicle Segmentation Method for Tracking A Model-based Vehicle Segmentation Method for Tracing Xuefeng Song Ram Nevatia Institute for Robotics and Intelligence System University of Southern California, Los Angeles, CA 90089-0273, USA {xsong nevatia}@usc.edu

More information

Evaluating the Performance of Systems for Tracking Football Players and Ball

Evaluating the Performance of Systems for Tracking Football Players and Ball Evaluating the Performance of Systems for Tracking Football Players and Ball Y. Li A. Dore J. Orwell School of Computing D.I.B.E. School of Computing Kingston University University of Genova Kingston University

More information

Real-Time Tracking of Pedestrians and Vehicles

Real-Time Tracking of Pedestrians and Vehicles Real-Time Tracking of Pedestrians and Vehicles N.T. Siebel and S.J. Maybank. Computational Vision Group Department of Computer Science The University of Reading Reading RG6 6AY, England Abstract We present

More information

Speed Performance Improvement of Vehicle Blob Tracking System

Speed Performance Improvement of Vehicle Blob Tracking System Speed Performance Improvement of Vehicle Blob Tracking System Sung Chun Lee and Ram Nevatia University of Southern California, Los Angeles, CA 90089, USA sungchun@usc.edu, nevatia@usc.edu Abstract. A speed

More information

CCTV - Video Analytics for Traffic Management

CCTV - Video Analytics for Traffic Management CCTV - Video Analytics for Traffic Management Index Purpose Description Relevance for Large Scale Events Technologies Impacts Integration potential Implementation Best Cases and Examples 1 of 12 Purpose

More information

The goal is multiply object tracking by detection with application on pedestrians.

The goal is multiply object tracking by detection with application on pedestrians. Coupled Detection and Trajectory Estimation for Multi-Object Tracking By B. Leibe, K. Schindler, L. Van Gool Presented By: Hanukaev Dmitri Lecturer: Prof. Daphna Wienshall The Goal The goal is multiply

More information

Face Recognition in Low-resolution Images by Using Local Zernike Moments

Face Recognition in Low-resolution Images by Using Local Zernike Moments Proceedings of the International Conference on Machine Vision and Machine Learning Prague, Czech Republic, August14-15, 014 Paper No. 15 Face Recognition in Low-resolution Images by Using Local Zernie

More information

3D Vehicle Extraction and Tracking from Multiple Viewpoints for Traffic Monitoring by using Probability Fusion Map

3D Vehicle Extraction and Tracking from Multiple Viewpoints for Traffic Monitoring by using Probability Fusion Map Electronic Letters on Computer Vision and Image Analysis 7(2):110-119, 2008 3D Vehicle Extraction and Tracking from Multiple Viewpoints for Traffic Monitoring by using Probability Fusion Map Zhencheng

More information

Support Vector Machine-Based Human Behavior Classification in Crowd through Projection and Star Skeletonization

Support Vector Machine-Based Human Behavior Classification in Crowd through Projection and Star Skeletonization Journal of Computer Science 6 (9): 1008-1013, 2010 ISSN 1549-3636 2010 Science Publications Support Vector Machine-Based Human Behavior Classification in Crowd through Projection and Star Skeletonization

More information

A Reliability Point and Kalman Filter-based Vehicle Tracking Technique

A Reliability Point and Kalman Filter-based Vehicle Tracking Technique A Reliability Point and Kalman Filter-based Vehicle Tracing Technique Soo Siang Teoh and Thomas Bräunl Abstract This paper introduces a technique for tracing the movement of vehicles in consecutive video

More information

A PHOTOGRAMMETRIC APPRAOCH FOR AUTOMATIC TRAFFIC ASSESSMENT USING CONVENTIONAL CCTV CAMERA

A PHOTOGRAMMETRIC APPRAOCH FOR AUTOMATIC TRAFFIC ASSESSMENT USING CONVENTIONAL CCTV CAMERA A PHOTOGRAMMETRIC APPRAOCH FOR AUTOMATIC TRAFFIC ASSESSMENT USING CONVENTIONAL CCTV CAMERA N. Zarrinpanjeh a, F. Dadrassjavan b, H. Fattahi c * a Islamic Azad University of Qazvin - nzarrin@qiau.ac.ir

More information

False alarm in outdoor environments

False alarm in outdoor environments Accepted 1.0 Savantic letter 1(6) False alarm in outdoor environments Accepted 1.0 Savantic letter 2(6) Table of contents Revision history 3 References 3 1 Introduction 4 2 Pre-processing 4 3 Detection,

More information

Algorithm (DCABES 2009)

Algorithm (DCABES 2009) People Tracking via a Modified CAMSHIFT Algorithm (DCABES 2009) Fahad Fazal Elahi Guraya, Pierre-Yves Bayle and Faouzi Alaya Cheikh Department of Computer Science and Media Technology, Gjovik University

More information

Vision based approach to human fall detection

Vision based approach to human fall detection Vision based approach to human fall detection Pooja Shukla, Arti Tiwari CSVTU University Chhattisgarh, poojashukla2410@gmail.com 9754102116 Abstract Day by the count of elderly people living alone at home

More information

Human behavior analysis from videos using optical flow

Human behavior analysis from videos using optical flow L a b o r a t o i r e I n f o r m a t i q u e F o n d a m e n t a l e d e L i l l e Human behavior analysis from videos using optical flow Yassine Benabbas Directeur de thèse : Chabane Djeraba Multitel

More information

Multi-view Intelligent Vehicle Surveillance System

Multi-view Intelligent Vehicle Surveillance System Multi-view Intelligent Vehicle Surveillance System S. Denman, C. Fookes, J. Cook, C. Davoren, A. Mamic, G. Farquharson, D. Chen, B. Chen and S. Sridharan Image and Video Research Laboratory Queensland

More information

Video Surveillance System for Security Applications

Video Surveillance System for Security Applications Video Surveillance System for Security Applications Vidya A.S. Department of CSE National Institute of Technology Calicut, Kerala, India V. K. Govindan Department of CSE National Institute of Technology

More information

Tracking and integrated navigation Konrad Schindler

Tracking and integrated navigation Konrad Schindler Tracking and integrated navigation Konrad Schindler Institute of Geodesy and Photogrammetry Tracking Navigation needs predictions for dynamic objects estimate trajectories in 3D world coordinates and extrapolate

More information

Online Learned Discriminative Part-Based Appearance Models for Multi-Human Tracking

Online Learned Discriminative Part-Based Appearance Models for Multi-Human Tracking Online Learned Discriminative Part-Based Appearance Models for Multi-Human Tracing Bo Yang and Ram Nevatia Institute for Robotics and Intelligent Systems, University of Southern California Los Angeles,

More information

Group Members: Nuri Murat Arar Fatma Güney Aytuğ Murat Aydın M. Sami Arpa Erkam Akkurt. Asst. Prof. Dr. Pınar Duygulu Şahin

Group Members: Nuri Murat Arar Fatma Güney Aytuğ Murat Aydın M. Sami Arpa Erkam Akkurt. Asst. Prof. Dr. Pınar Duygulu Şahin Group Members: Nuri Murat Arar Fatma Güney Aytuğ Murat Aydın M. Sami Arpa Erkam Akkurt Supervisor: Jury Members: Asst. Prof. Dr. Selim Aksoy Prof. Dr. H. Altay Güvenir Asst. Prof. Dr. Pınar Duygulu Şahin

More information

Towards License Plate Recognition: Comparying Moving Objects Segmentation Approaches

Towards License Plate Recognition: Comparying Moving Objects Segmentation Approaches 1 Towards License Plate Recognition: Comparying Moving Objects Segmentation Approaches V. J. Oliveira-Neto, G. Cámara-Chávez, D. Menotti UFOP - Federal University of Ouro Preto Computing Department Ouro

More information

Tracking and Recognition in Sports Videos

Tracking and Recognition in Sports Videos Tracking and Recognition in Sports Videos Mustafa Teke a, Masoud Sattari b a Graduate School of Informatics, Middle East Technical University, Ankara, Turkey mustafa.teke@gmail.com b Department of Computer

More information

Optical Flow as a property of moving objects used for their registration

Optical Flow as a property of moving objects used for their registration Optical Flow as a property of moving objects used for their registration Wolfgang Schulz Computer Vision Course Project York University Email:wschulz@cs.yorku.ca 1. Introduction A soccer game is a real

More information

Tracking Groups of Pedestrians in Video Sequences

Tracking Groups of Pedestrians in Video Sequences Tracking Groups of Pedestrians in Video Sequences Jorge S. Marques Pedro M. Jorge Arnaldo J. Abrantes J. M. Lemos IST / ISR ISEL / IST ISEL INESC-ID / IST Lisbon, Portugal Lisbon, Portugal Lisbon, Portugal

More information

Vision Based Traffic Light Triggering for Motorbikes

Vision Based Traffic Light Triggering for Motorbikes Vision Based Traffic Light Triggering for Motorbikes Tommy Chheng Department of Computer Science and Engineering University of California, San Diego tcchheng@ucsd.edu Abstract Current traffic light triggering

More information

Detection and Recognition of Mixed Traffic for Driver Assistance System

Detection and Recognition of Mixed Traffic for Driver Assistance System Detection and Recognition of Mixed Traffic for Driver Assistance System Pradnya Meshram 1, Prof. S.S. Wankhede 2 1 Scholar, Department of Electronics Engineering, G.H.Raisoni College of Engineering, Digdoh

More information

University of Leeds SCHOOL OF COMPUTER STUDIES RESEARCH REPORT SERIES Report 2001.21

University of Leeds SCHOOL OF COMPUTER STUDIES RESEARCH REPORT SERIES Report 2001.21 University of Leeds SCHOOL OF COMPUTER STUDIES RESEARCH REPORT SERIES Report 2001.21 Tracking Multiple Vehicles using Foreground, Background and Motion Models 1 by D R Magee December 2001 1 Submitted to

More information

Neovision2 Performance Evaluation Protocol

Neovision2 Performance Evaluation Protocol Neovision2 Performance Evaluation Protocol Version 3.0 4/16/2012 Public Release Prepared by Rajmadhan Ekambaram rajmadhan@mail.usf.edu Dmitry Goldgof, Ph.D. goldgof@cse.usf.edu Rangachar Kasturi, Ph.D.

More information

Traffic Monitoring Systems. Technology and sensors

Traffic Monitoring Systems. Technology and sensors Traffic Monitoring Systems Technology and sensors Technology Inductive loops Cameras Lidar/Ladar and laser Radar GPS etc Inductive loops Inductive loops signals Inductive loop sensor The inductance signal

More information

Colorado School of Mines Computer Vision Professor William Hoff

Colorado School of Mines Computer Vision Professor William Hoff Professor William Hoff Dept of Electrical Engineering &Computer Science http://inside.mines.edu/~whoff/ 1 Introduction to 2 What is? A process that produces from images of the external world a description

More information

EXPLORING IMAGE-BASED CLASSIFICATION TO DETECT VEHICLE MAKE AND MODEL FINAL REPORT

EXPLORING IMAGE-BASED CLASSIFICATION TO DETECT VEHICLE MAKE AND MODEL FINAL REPORT EXPLORING IMAGE-BASED CLASSIFICATION TO DETECT VEHICLE MAKE AND MODEL FINAL REPORT Jeffrey B. Flora, Mahbubul Alam, Amr H. Yousef, and Khan M. Iftekharuddin December 2013 DISCLAIMER The contents of this

More information

Behavior Analysis in Crowded Environments. XiaogangWang Department of Electronic Engineering The Chinese University of Hong Kong June 25, 2011

Behavior Analysis in Crowded Environments. XiaogangWang Department of Electronic Engineering The Chinese University of Hong Kong June 25, 2011 Behavior Analysis in Crowded Environments XiaogangWang Department of Electronic Engineering The Chinese University of Hong Kong June 25, 2011 Behavior Analysis in Sparse Scenes Zelnik-Manor & Irani CVPR

More information

An Energy-Based Vehicle Tracking System using Principal Component Analysis and Unsupervised ART Network

An Energy-Based Vehicle Tracking System using Principal Component Analysis and Unsupervised ART Network Proceedings of the 8th WSEAS Int. Conf. on ARTIFICIAL INTELLIGENCE, KNOWLEDGE ENGINEERING & DATA BASES (AIKED '9) ISSN: 179-519 435 ISBN: 978-96-474-51-2 An Energy-Based Vehicle Tracking System using Principal

More information

Method for Traffic Flow Estimation using Ondashboard

Method for Traffic Flow Estimation using Ondashboard Method for Traffic Flow Estimation using Ondashboard Camera Image Kohei Arai Graduate School of Science and Engineering Saga University Saga, Japan Steven Ray Sentinuwo Department of Electrical Engineering

More information

A feature-based tracking algorithm for vehicles in intersections

A feature-based tracking algorithm for vehicles in intersections A feature-based tracking algorithm for vehicles in intersections Nicolas Saunier and Tarek Sayed Departement of Civil Engineering, University of British Columbia 6250 Applied Science Lane, Vancouver BC

More information

Tracking in flussi video 3D. Ing. Samuele Salti

Tracking in flussi video 3D. Ing. Samuele Salti Seminari XXIII ciclo Tracking in flussi video 3D Ing. Tutors: Prof. Tullio Salmon Cinotti Prof. Luigi Di Stefano The Tracking problem Detection Object model, Track initiation, Track termination, Tracking

More information

Using geometry and related things

Using geometry and related things Using geometry and related things Region labels + Boundaries and objects Stronger geometric constraints from domain knowledge Reasoning on aspects and poses 3D point clouds Qualitative More quantitative

More information

VEHICLE TRACKING USING ACOUSTIC AND VIDEO SENSORS

VEHICLE TRACKING USING ACOUSTIC AND VIDEO SENSORS VEHICLE TRACKING USING ACOUSTIC AND VIDEO SENSORS Aswin C Sankaranayanan, Qinfen Zheng, Rama Chellappa University of Maryland College Park, MD - 277 {aswch, qinfen, rama}@cfar.umd.edu Volkan Cevher, James

More information

A ROBUST BACKGROUND REMOVAL ALGORTIHMS

A ROBUST BACKGROUND REMOVAL ALGORTIHMS A ROBUST BACKGROUND REMOVAL ALGORTIHMS USING FUZZY C-MEANS CLUSTERING ABSTRACT S.Lakshmi 1 and Dr.V.Sankaranarayanan 2 1 Jeppiaar Engineering College, Chennai lakshmi1503@gmail.com 2 Director, Crescent

More information

VSSN 06 Algorithm Competition

VSSN 06 Algorithm Competition VSSN 06 Algorithm Competition 27. Oct. 2006 Eva Hörster, Rainer Lienhart Multimedia Computing Lab University of Augsburg, Germany Goals Get a valuable resource for the research community Foster and accelerate

More information

Vision-Based Blind Spot Detection Using Optical Flow

Vision-Based Blind Spot Detection Using Optical Flow Vision-Based Blind Spot Detection Using Optical Flow M.A. Sotelo 1, J. Barriga 1, D. Fernández 1, I. Parra 1, J.E. Naranjo 2, M. Marrón 1, S. Alvarez 1, and M. Gavilán 1 1 Department of Electronics, University

More information

Street Viewer: An Autonomous Vision Based Traffic Tracking System. Andrea Bottino, Alessandro Garbo, Carmelo Loiacono and Stefano Quer *

Street Viewer: An Autonomous Vision Based Traffic Tracking System. Andrea Bottino, Alessandro Garbo, Carmelo Loiacono and Stefano Quer * sensors Article Street Viewer: An Autonomous Vision Based Traffic Tracking System Andrea Bottino, Alessandro Garbo, Carmelo Loiacono and Stefano Quer * Dipartimento di Automatica ed Informatica, Politecnico

More information

Visual Vehicle Tracking Using An Improved EKF*

Visual Vehicle Tracking Using An Improved EKF* ACCV: he 5th Asian Conference on Computer Vision, 3--5 January, Melbourne, Australia Visual Vehicle racing Using An Improved EKF* Jianguang Lou, ao Yang, Weiming u, ieniu an National Laboratory of Pattern

More information

Vehicle Tracking System Robust to Changes in Environmental Conditions

Vehicle Tracking System Robust to Changes in Environmental Conditions INORMATION & COMMUNICATIONS Vehicle Tracking System Robust to Changes in Environmental Conditions Yasuo OGIUCHI*, Masakatsu HIGASHIKUBO, Kenji NISHIDA and Takio KURITA Driving Safety Support Systems (DSSS)

More information

Traffic Flow Monitoring in Crowded Cities

Traffic Flow Monitoring in Crowded Cities Traffic Flow Monitoring in Crowded Cities John A. Quinn and Rose Nakibuule Faculty of Computing & I.T. Makerere University P.O. Box 7062, Kampala, Uganda {jquinn,rnakibuule}@cit.mak.ac.ug Abstract Traffic

More information

Color-Based Road Detection and its Evaluation on the KITTI Road Benchmark

Color-Based Road Detection and its Evaluation on the KITTI Road Benchmark Color-Based Road Detection and its Evaluation on the KITTI Road Benchmark Bihao WANG 1,2, Vincent Frémont 1,2, Sergio Alberto Rodríguez Florez 3,4 1 Université de Technologie de Compiègne (UTC) 2 CNRS

More information

Keywords: History-based tracker weighted directed graph, vehicle tracking, spatio-temporal data base, top-down tracker.

Keywords: History-based tracker weighted directed graph, vehicle tracking, spatio-temporal data base, top-down tracker. Vehicle Vehicle Tracing by by a Motion a Motion History History Graph Graph motion analysis. Vehicle tracing has been extensively utilized in traffic scene analysis. Hadi Hadi Sadoghi Sadoghi Yazdi Yazdi

More information

The Design and Implementation of Traffic Accident Identification System Based on Video

The Design and Implementation of Traffic Accident Identification System Based on Video 3rd International Conference on Multimedia Technology(ICMT 2013) The Design and Implementation of Traffic Accident Identification System Based on Video Chenwei Xiang 1, Tuo Wang 2 Abstract: With the rapid

More information

Environmental Remote Sensing GEOG 2021

Environmental Remote Sensing GEOG 2021 Environmental Remote Sensing GEOG 2021 Lecture 4 Image classification 2 Purpose categorising data data abstraction / simplification data interpretation mapping for land cover mapping use land cover class

More information

3D Model based Object Class Detection in An Arbitrary View

3D Model based Object Class Detection in An Arbitrary View 3D Model based Object Class Detection in An Arbitrary View Pingkun Yan, Saad M. Khan, Mubarak Shah School of Electrical Engineering and Computer Science University of Central Florida http://www.eecs.ucf.edu/

More information

Neural Network based Vehicle Classification for Intelligent Traffic Control

Neural Network based Vehicle Classification for Intelligent Traffic Control Neural Network based Vehicle Classification for Intelligent Traffic Control Saeid Fazli 1, Shahram Mohammadi 2, Morteza Rahmani 3 1,2,3 Electrical Engineering Department, Zanjan University, Zanjan, IRAN

More information

Vision-Based Pedestrian Detection for Driving Assistance

Vision-Based Pedestrian Detection for Driving Assistance Vision-Based Pedestrian Detection for Driving Assistance Literature Survey Multidimensional DSP Project, Spring 2005 Marco Perez Abstract This survey focuses on some of the most important and recent algorithms

More information

EFFICIENT VEHICLE TRACKING AND CLASSIFICATION FOR AN AUTOMATED TRAFFIC SURVEILLANCE SYSTEM

EFFICIENT VEHICLE TRACKING AND CLASSIFICATION FOR AN AUTOMATED TRAFFIC SURVEILLANCE SYSTEM EFFICIENT VEHICLE TRACKING AND CLASSIFICATION FOR AN AUTOMATED TRAFFIC SURVEILLANCE SYSTEM Amol Ambardekar, Mircea Nicolescu, and George Bebis Department of Computer Science and Engineering University

More information

Automatic Traffic Estimation Using Image Processing

Automatic Traffic Estimation Using Image Processing Automatic Traffic Estimation Using Image Processing Pejman Niksaz Science &Research Branch, Azad University of Yazd, Iran Pezhman_1366@yahoo.com Abstract As we know the population of city and number of

More information

Rafael Martín & José M. Martínez

Rafael Martín & José M. Martínez A semi-supervised system for players detection and tracking in multi-camera soccer videos Rafael Martín José M. Martínez Multimedia Tools and Applications An International Journal ISSN 1380-7501 DOI 10.1007/s11042-013-1659-6

More information

Novel Probabilistic Methods for Visual Surveillance Applications

Novel Probabilistic Methods for Visual Surveillance Applications University of Pannonia Information Science and Technology PhD School Thesis Booklet Novel Probabilistic Methods for Visual Surveillance Applications Ákos Utasi Department of Electrical Engineering and

More information

Human and Moving Object Detection and Tracking Using Image Processing

Human and Moving Object Detection and Tracking Using Image Processing International Journal of Engineering and Technical Research (IJETR) ISSN: 2321-0869, Volume-2, Issue-3, March 2014 Human and Moving Object Detection and Tracking Using Image Processing Akash V. Kavitkar,

More information

Real-time Person Detection and Tracking in Panoramic Video

Real-time Person Detection and Tracking in Panoramic Video 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops Real-time Person Detection and Tracking in Panoramic Video Marcus Thaler, Werner Bailer JOANNEUM RESEARCH, DIGITAL Institute for

More information

Automatic Labeling of Lane Markings for Autonomous Vehicles

Automatic Labeling of Lane Markings for Autonomous Vehicles Automatic Labeling of Lane Markings for Autonomous Vehicles Jeffrey Kiske Stanford University 450 Serra Mall, Stanford, CA 94305 jkiske@stanford.edu 1. Introduction As autonomous vehicles become more popular,

More information

IMPLICIT SHAPE MODELS FOR OBJECT DETECTION IN 3D POINT CLOUDS

IMPLICIT SHAPE MODELS FOR OBJECT DETECTION IN 3D POINT CLOUDS IMPLICIT SHAPE MODELS FOR OBJECT DETECTION IN 3D POINT CLOUDS Alexander Velizhev 1 (presenter) Roman Shapovalov 2 Konrad Schindler 3 1 Hexagon Technology Center, Heerbrugg, Switzerland 2 Graphics & Media

More information

IMPROVED VIRTUAL MOUSE POINTER USING KALMAN FILTER BASED GESTURE TRACKING TECHNIQUE

IMPROVED VIRTUAL MOUSE POINTER USING KALMAN FILTER BASED GESTURE TRACKING TECHNIQUE 39 IMPROVED VIRTUAL MOUSE POINTER USING KALMAN FILTER BASED GESTURE TRACKING TECHNIQUE D.R.A.M. Dissanayake, U.K.R.M.H. Rajapaksha 2 and M.B Dissanayake 3 Department of Electrical and Electronic Engineering,

More information

Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite

Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite Philip Lenz 1 Andreas Geiger 2 Christoph Stiller 1 Raquel Urtasun 3 1 KARLSRUHE INSTITUTE OF TECHNOLOGY 2 MAX-PLANCK-INSTITUTE IS 3

More information

SAFE/T Tool for Analyzing Driver Video Data

SAFE/T Tool for Analyzing Driver Video Data SAFE/T Tool for Analyzing Driver Video Data Part 2: Software Development Carol Martell, Rob Foss UNC Highway Safety Research Center Ken Gish, Loren Staplin TransAnalytics Objective Overall: Develop analytic

More information

The Visual Internet of Things System Based on Depth Camera

The Visual Internet of Things System Based on Depth Camera The Visual Internet of Things System Based on Depth Camera Xucong Zhang 1, Xiaoyun Wang and Yingmin Jia Abstract The Visual Internet of Things is an important part of information technology. It is proposed

More information

Learning Detectors from Large Datasets for Object Retrieval in Video Surveillance

Learning Detectors from Large Datasets for Object Retrieval in Video Surveillance 2012 IEEE International Conference on Multimedia and Expo Learning Detectors from Large Datasets for Object Retrieval in Video Surveillance Rogerio Feris, Sharath Pankanti IBM T. J. Watson Research Center

More information

Real-Time People Localization and Tracking through Fixed Stereo Vision

Real-Time People Localization and Tracking through Fixed Stereo Vision Proc. of International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems (IEA/AIE), 2005 Real-Time People Localization and Tracking through Fixed Stereo Vision

More information

A Learning Based Method for Super-Resolution of Low Resolution Images

A Learning Based Method for Super-Resolution of Low Resolution Images A Learning Based Method for Super-Resolution of Low Resolution Images Emre Ugur June 1, 2004 emre.ugur@ceng.metu.edu.tr Abstract The main objective of this project is the study of a learning based method

More information

Visual Tracking of Athletes in Volleyball Sport Videos

Visual Tracking of Athletes in Volleyball Sport Videos Visual Tracking of Athletes in Volleyball Sport Videos H.Salehifar 1 and A.Bastanfard 2 1 Faculty of Electrical, Computer and IT, Islamic Azad University, Qazvin Branch, Qazvin, Iran 2 Computer Group,

More information

Real-Time Traffic Flow Analysis without Background Modeling

Real-Time Traffic Flow Analysis without Background Modeling Real-Time Traffic Flow Analysis without Background Modeling Cheng-Chang Lien and Ming-Hsiu Tsai Department of Computer Science and Information Engineering, Chung Hua University, Hsinchu, Taiwan, R.O.C.

More information

Real time vehicle detection and tracking on multiple lanes

Real time vehicle detection and tracking on multiple lanes Real time vehicle detection and tracking on multiple lanes Kristian Kovačić Edouard Ivanjko Hrvoje Gold Department of Intelligent Transportation Systems Faculty of Transport and Traffic Sciences University

More information

Reconstructing 3D Pose and Motion from a Single Camera View

Reconstructing 3D Pose and Motion from a Single Camera View Reconstructing 3D Pose and Motion from a Single Camera View R Bowden, T A Mitchell and M Sarhadi Brunel University, Uxbridge Middlesex UB8 3PH richard.bowden@brunel.ac.uk Abstract This paper presents a

More information

RISING traffic levels and increasingly busier roads are a

RISING traffic levels and increasingly busier roads are a 188 IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 7, NO. 2, JUNE 2006 Detection and Classification of Highway Lanes Using Vehicle Motion Trajectories José Melo, Andrew Naftel, Member, IEEE,

More information

A New Robust Algorithm for Video Text Extraction

A New Robust Algorithm for Video Text Extraction A New Robust Algorithm for Video Text Extraction Pattern Recognition, vol. 36, no. 6, June 2003 Edward K. Wong and Minya Chen School of Electrical Engineering and Computer Science Kyungpook National Univ.

More information

Computer Vision Algorithms for Intersection Monitoring

Computer Vision Algorithms for Intersection Monitoring 78 IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 4, NO. 2, JUNE 2003 Computer Vision Algorithms for Intersection Monitoring Harini Veeraraghavan, Osama Masoud, and Nikolaos P. Papanikolopoulos,

More information

QUANTITATIVE MEASUREMENT OF TEAMWORK IN BALL GAMES USING DOMINANT REGION

QUANTITATIVE MEASUREMENT OF TEAMWORK IN BALL GAMES USING DOMINANT REGION QUANTITATIVE MEASUREMENT OF TEAMWORK IN BALL GAMES USING DOMINANT REGION Tsuyoshi TAKI and Jun-ichi HASEGAWA Chukyo University, Japan School of Computer and Cognitive Sciences {taki,hasegawa}@sccs.chukyo-u.ac.jp

More information

Distributed Vision Processing in Smart Camera Networks

Distributed Vision Processing in Smart Camera Networks Distributed Vision Processing in Smart Camera Networks CVPR-07 Hamid Aghajan, Stanford University, USA François Berry, Univ. Blaise Pascal, France Horst Bischof, TU Graz, Austria Richard Kleihorst, NXP

More information

Component Ordering in Independent Component Analysis Based on Data Power

Component Ordering in Independent Component Analysis Based on Data Power Component Ordering in Independent Component Analysis Based on Data Power Anne Hendrikse Raymond Veldhuis University of Twente University of Twente Fac. EEMCS, Signals and Systems Group Fac. EEMCS, Signals

More information

Mean-Shift Tracking with Random Sampling

Mean-Shift Tracking with Random Sampling 1 Mean-Shift Tracking with Random Sampling Alex Po Leung, Shaogang Gong Department of Computer Science Queen Mary, University of London, London, E1 4NS Abstract In this work, boosting the efficiency of

More information

Automatic Vehicle Detection, Tracking and Recognition of License Plate in Real Time Videos Lucky Kodwani

Automatic Vehicle Detection, Tracking and Recognition of License Plate in Real Time Videos Lucky Kodwani Automatic Vehicle Detection, Tracking and Recognition of License Plate in Real Time Videos Lucky Kodwani Department of Electronics and Communication Engineering National Institute of Technology Rourkela

More information

Big Data: Image & Video Analytics

Big Data: Image & Video Analytics Big Data: Image & Video Analytics How it could support Archiving & Indexing & Searching Dieter Haas, IBM Deutschland GmbH The Big Data Wave 60% of internet traffic is multimedia content (images and videos)

More information

Vehicle Tracking by Simultaneous Detection and Viewpoint Estimation

Vehicle Tracking by Simultaneous Detection and Viewpoint Estimation Vehicle Tracking by Simultaneous Detection and Viewpoint Estimation Ricardo Guerrero-Gómez-Olmedo, Roberto López-Sastre, Saturnino Maldonado-Bascón, and Antonio Fernández-Caballero 2 GRAM, Department of

More information

Tracking And Object Classification For Automated Surveillance

Tracking And Object Classification For Automated Surveillance Tracking And Object Classification For Automated Surveillance Omar Javed and Mubarak Shah Computer Vision ab, University of Central Florida, 4000 Central Florida Blvd, Orlando, Florida 32816, USA {ojaved,shah}@cs.ucf.edu

More information

Predictive and Probabilistic Tracking to Detect Stopped Vehicles

Predictive and Probabilistic Tracking to Detect Stopped Vehicles Predictive and Probabilistic Tracking to Detect Stopped Vehicles Rudy Melli, Andrea Prati, Rita Cucchiara D.I.I. - University of Modena and Reggio Emilia Via Vignolese, 905/b I-41100 Modena, Italy Lieven

More information

Advanced Methods for Pedestrian and Bicyclist Sensing

Advanced Methods for Pedestrian and Bicyclist Sensing Advanced Methods for Pedestrian and Bicyclist Sensing Yinhai Wang PacTrans STAR Lab University of Washington Email: yinhai@uw.edu Tel: 1-206-616-2696 For Exchange with University of Nevada Reno Sept. 25,

More information

Pedestrian Detection with RCNN

Pedestrian Detection with RCNN Pedestrian Detection with RCNN Matthew Chen Department of Computer Science Stanford University mcc17@stanford.edu Abstract In this paper we evaluate the effectiveness of using a Region-based Convolutional

More information

Interactive person re-identification in TV series

Interactive person re-identification in TV series Interactive person re-identification in TV series Mika Fischer Hazım Kemal Ekenel Rainer Stiefelhagen CV:HCI lab, Karlsruhe Institute of Technology Adenauerring 2, 76131 Karlsruhe, Germany E-mail: {mika.fischer,ekenel,rainer.stiefelhagen}@kit.edu

More information

CS231M Project Report - Automated Real-Time Face Tracking and Blending

CS231M Project Report - Automated Real-Time Face Tracking and Blending CS231M Project Report - Automated Real-Time Face Tracking and Blending Steven Lee, slee2010@stanford.edu June 6, 2015 1 Introduction Summary statement: The goal of this project is to create an Android

More information

Automatic parameter regulation for a tracking system with an auto-critical function

Automatic parameter regulation for a tracking system with an auto-critical function Automatic parameter regulation for a tracking system with an auto-critical function Daniela Hall INRIA Rhône-Alpes, St. Ismier, France Email: Daniela.Hall@inrialpes.fr Abstract In this article we propose

More information

Video Analytics A New Standard

Video Analytics A New Standard Benefits The system offers the following overall benefits: Tracker High quality tracking engine UDP s embedded intelligent Video Analytics software is fast becoming the standard for all surveillance and

More information

Object tracking in video scenes

Object tracking in video scenes A Seminar On Object tracking in video scenes Presented by Alok K. Watve M.Tech. IT 1st year Indian Institue of Technology, Kharagpur Under the guidance of Dr. Shamik Sural Assistant Professor School of

More information

Robust Panoramic Image Stitching

Robust Panoramic Image Stitching Robust Panoramic Image Stitching CS231A Final Report Harrison Chau Department of Aeronautics and Astronautics Stanford University Stanford, CA, USA hwchau@stanford.edu Robert Karol Department of Aeronautics

More information

Crowdclustering with Sparse Pairwise Labels: A Matrix Completion Approach

Crowdclustering with Sparse Pairwise Labels: A Matrix Completion Approach Outline Crowdclustering with Sparse Pairwise Labels: A Matrix Completion Approach Jinfeng Yi, Rong Jin, Anil K. Jain, Shaili Jain 2012 Presented By : KHALID ALKOBAYER Crowdsourcing and Crowdclustering

More information

Spatio-Temporal Nonparametric Background Modeling and Subtraction

Spatio-Temporal Nonparametric Background Modeling and Subtraction Spatio-Temporal Nonparametric Background Modeling and Subtraction Raviteja Vemulapalli and R. Aravind Department of Electrical engineering Indian Institute of Technology, Madras Background subtraction

More information

The KITTI-ROAD Evaluation Benchmark. for Road Detection Algorithms

The KITTI-ROAD Evaluation Benchmark. for Road Detection Algorithms The KITTI-ROAD Evaluation Benchmark for Road Detection Algorithms 08.06.2014 Jannik Fritsch Honda Research Institute Europe, Offenbach, Germany Presented material created together with Tobias Kuehnl Research

More information

Computer Vision - part II

Computer Vision - part II Computer Vision - part II Review of main parts of Section B of the course School of Computer Science & Statistics Trinity College Dublin Dublin 2 Ireland www.scss.tcd.ie Lecture Name Course Name 1 1 2

More information

Real-time Stereo Vision Obstacle Detection for Automotive Safety Application

Real-time Stereo Vision Obstacle Detection for Automotive Safety Application Real-time Stereo Vision Obstacle Detection for Automotive Safety Application D. Perrone L. Iocchi P.C. Antonello Sapienza University of Rome Dipartimento di Informatica e Sistemistica E-mail: dperrone@it.gnu.org,

More information

Tracking based on graph of pairs of plots

Tracking based on graph of pairs of plots INFORMATIK 0 - Informati schafft Communities 4. Jahrestagung der Gesellschaft für Informati, 4.-7.0.0, Berlin www.informati0.de Tracing based on graph of pairs of plots Frédéric Livernet, Aline Campillo-Navetti

More information