Speed Performance Improvement of Vehicle Blob Tracking System
|
|
- Suzan Bradley
- 2 years ago
- Views:
Transcription
1 Speed Performance Improvement of Vehicle Blob Tracking System Sung Chun Lee and Ram Nevatia University of Southern California, Los Angeles, CA 90089, USA Abstract. A speed performance improved vehicle tracking system on a given set of evaluation videos of a street surveillance system is presented. We implement multi-threading technique to meet the requirement of realtime performance which demanded in the practical surveillance systems. Through multi-threading technique, we can accomplish near real-time performance. An analysis of results is also presented. 1 Introduction Detection and tracking of vehicles is important for traffic analysis and video surveillance. Our objective is to develop techniques for automatic vehicle tracking to enable the image analysts to cope with exploiting the exponentially growing volume of video imagery available to them. There are several factors that make this task highly challenging: camera and scene instabilities. Firstly, sometimes, cameras shake, creating false foreground detections and automatic gain control abruptly changes the intensity of the video image causing multiple false detections. Secondly, there exist ambient illumination changes such as due to passing clouds and occluding objects such as trees and traffic signs as illustrated in Fig. 1. (a) (b) Fig. 1. Illustration of camera and scene instabilities: (a) A white car (top right) occluded behind a tree and a traffic sign, (b) Intensity of scene dramatically changes due to automatic camera gain change as clouds pass R. Stiefelhagen et al. (Eds.): CLEAR 2007 and RT 2007, LNCS 4625, pp , c Springer-Verlag Berlin Heidelberg 2008
2 198 S.C. Lee and R. Nevatia In our previous work [5], we have tried to address these issues and described a robust detection and tracking vehicle system. One of limitations of the work is slow computational performance. Practical surveillance applications demand real time processing performance as an important factor. We applied a multithreading programming approach to improve the computational performance which is the focus of this paper. 2 Vehicle Tracking and Detection Before explaining the speed enhancement, we briefly describe the detection and tracking algorithm from the previous work. The task is to evaluate the performance of our vehicle detection and tracking algorithm on a set of surveillance videos provided by the VACE project [2]. Objective includes accurate detection, localization and tracking while maintaining the identities vehicles as they travel across different frames. The videos are of street scenes captured by cameras mounted at light pole heights looking down towards the ground. There is one road running from top to bottom of the image and another one from left to right near the top of the image. Provided videos are from two different cameras at several different times. We assume vehicles move on a ground plane; we use vehicle motion and vanishing points from scene features to compute an approximate camera model [4]. This process is performed once, in training phase. To distinguish vehicles from walking humans and other motion, we set a minimum size for an object to be considered as a vehicle; this size is set in 3D, its size in image is computed by using the camera parameters. 2.1 Background Subtraction A common way to detect moving objects has been to learn a pixel-wise model of the background and to flag significant variations from it as a moving object occludes the pixel. This approach works well when the camera is stationary. We have used this method to detect and track vehicle blobs in surveillance videos. We use an adaptive background subtraction method [4]. We first detect temporal color changes in the input video stream and classify as corresponding to moving objects. Then, we filter out isolated points and cluster the detected pixels to form foreground objects. The background model is updated at each frame. In an ideal case, every extracted foreground object would correspond to one vehicle object. However, there might be other cases: merged vehicles, split vehicle, or other moving objects (e.g. human). 2.2 Vehicle Tracking and Detection Our tracking method processes the frames sequentially. We analyze the extracted foreground blobs from background subtraction process as described in Section 2.1. At each new frame, we do following:
3 Speed Performance Improvement of Vehicle Blob Tracking System Apply tracking object s dynamic model to predict its new position 2. Generate an association matrix based on the overlap between the predicted object rectangle and the detected blob rectangle 3. In the association matrix, there are following cases: (a) If the track-blob match is one-to-one, we simply update the position of the track. (b) If a blob has no match with current tracking objects and its size is comparable to a regular vehicle, a new tracking object is created. (c) If there is no blob match for more than a certain number of frames, the track of this object ends. (d) If an object matches with multiple blobs, we combine the split blobs into one to match with the object being tracked. (e) If multiple objects merge into one blob, we segment the blob based on the appearance model of each involved object. Specifically, we apply a meanshift color tracking method [1] to locate the vehicle in the merged blob. Some example results are given below in Fig. 2. Fig. 2. Results of vehicle tracking and detection (Yellow box: DCO (Dont Care Object), Green box: Detected ground truth, White box: Detected system output, Blue box: Missed ground truth, Black box: Detected but overlapped with DCO, Magenta box: Occluded ground truth (DCO), Cyan box: Stationary ground truth (DCO))
4 200 S.C. Lee and R. Nevatia 3 Computation Time Improvement One of the limitations in our approaches was that computation performance was up to 10 times slower than real-time. Our analysis indicated that the adaptive background subtraction task is the most time consuming process in our system. One alternative is the use of a Graphic Processing Unit (GPU); GPUs are very powerful and relatively inexpensive. However, GPU programming can be difficult. Instead, we have chosen to explore the use of multi-processors available in a modern workstation (dual-core dual processors are now common, quad-core machines should become available shortly). 3.1 Multithreading Background Subtraction A thread is a unit of execution in an operating system. A process (or application program) consists of one or more threads and the scheduler in the operating system controls thread task distribution. In our system, the user can control the number of threads and the system divides input stream image into small piece of images based on the number of threads as illustrated in Fig. 3. Then, the system distributes each image region to the threads to perform the adaptive background subtraction operation for smaller size images. Lastly, the system collects and combines the results of background subtraction after all threads finish their tasks. The procedural description of the multi-threaded adaptive background subtraction algorithm is shown below: Capture a video frame and do the followings: 2 Threads 4 Threads 8 Threads 16 Threads Fig. 3. Image division based on the number of threads
5 Speed Performance Improvement of Vehicle Blob Tracking System Divide the frame image into multiple images with configuration of Fig. 3 according to the number of threads (user s input) 2. Distribute the segmented image to each thread and dispatch threads to perform the adaptive background subtraction task. 3. Wait until all threads have finished their tasks. 4. Combine all pieces of background subtracted images into one image. Our preliminary experiments indicate that we can achieve a speed up of between 2.5 and 4 by using various number of threads. The main limitation seems to be in limited Cache memory that is shared between two processors in dual core chip. 4 Experiments and Results In 2006, we tested our systems on the videos provided by the VACE project. The size of each frame is The experiments were finished on a regular PC with Intel Pentium 2.6GHZ CPU. The average processing time was 2.85 (frame per second). By using a multithreading background subtraction, We are able to achieve average of 11.9 (frame per second) as shown in Table 1. Table 1. Computation Time using multithreading skill with Intel Xeon CPU with two dual core 64 bit 3.73GHZ CPUs Num of Threads Background Subtraction Speed with Multithreading (second / frame) Tracking Speed (second / frame) Final Speed (second / frame) Final Speed (frame / second) We quantitively evaluated our system according to the requirements of the test process. The metrics shown evaluate both the detection and tracking performances. We summarize the metric definitions below; more details may be found in [2]. 1. MODP (Multiple Object Detection Precision) measures the position precision of single frame detections; 2. MODA (Multiple Object Detection Accuracy) combines the influence of miss detections and false alarms; 3. MOTP (Multiple Object Tracking Precision)measures the position precision at tracking level; 4. MOTA (Multiple Object Tracking Accuracy) is MODA at tracking level with consideration of ID switches. Table 2 lists the scores on 50 test video sequences. One observation from the table is that: the difference of MODP and MOTP, or MODA and MOTA is very
6 202 S.C. Lee and R. Nevatia Table 2. Evaluation scores on 50 test video sequences Average Average Average Average MODP MODA MOTP MOTA small for all the test video sequences. It is mainly because the penalty on object ID change is relatively small. Actually, there is a difference in the number of ID changes in the outputs of different subsets. However, the current defined MOTA and MOTP are not able to reflect this tracking error very well. 5 Conclusions We have presented evaluation results of the detection and tracking accuracy and precision and the speed performance improvement of our vehicle detection and tracking system on a provided set of surveillance videos. The data contains many highly challenging features. The performance of our system is promising though many shortcomings exist and shows near real-time operation. Acknowledgements. This research was funded by VACE program of the U.S. government. References 1. Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using mean shift. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp (2001) 2. Kasturi, R., Goldgof, D., Soundararajan, P., Manohar, V., Boonstra, M., Korzhova, V.: Performance Evaluation Protocal for Face, Person and Vehicle Detection Tracking in Video Analysis and Centent Extraction (VACE-II) CLEAR - Classification of Events, Activities and Relationships, DataandInformation/ClearEval Protocol v5.pdf 3. Liyuan Li, Weimin Huang, Irene Y.H. Gu, and Qi Tian. Foreground Object Detection from Videos Containing Complex Background. ACM Multimedia (2003) 4. Lv, F., Zhao, T., Nevatia, R.: Self-Calibration of a Camera from Video of a Walking Human. In: Proceedings of IEEE International Conference on Pattern Recognition (ICPR) (2002) 5. Song, X., Nevatia, R.: Robust Vehicle Blob Tracking with Split/Merge Handling. In: Stiefelhagen, R., Garofolo, J.S. (eds.) CLEAR LNCS, vol. 4122, pp Springer, Heidelberg (2007)
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.
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,
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
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
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
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
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
An Active Head Tracking System for Distance Education and Videoconferencing Applications
An Active Head Tracking System for Distance Education and Videoconferencing Applications Sami Huttunen and Janne Heikkilä Machine Vision Group Infotech Oulu and Department of Electrical and Information
Cloud-Empowered Multimedia Service: An Automatic Video Storytelling Tool
Cloud-Empowered Multimedia Service: An Automatic Video Storytelling Tool Joseph C. Tsai Foundation of Computer Science Lab. The University of Aizu Fukushima-ken, Japan jctsai@u-aizu.ac.jp Abstract Video
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
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,
Building an Advanced Invariant Real-Time Human Tracking System
UDC 004.41 Building an Advanced Invariant Real-Time Human Tracking System Fayez Idris 1, Mazen Abu_Zaher 2, Rashad J. Rasras 3, and Ibrahiem M. M. El Emary 4 1 School of Informatics and Computing, German-Jordanian
A General Framework for Tracking Objects in a Multi-Camera Environment
A General Framework for Tracking Objects in a Multi-Camera Environment Karlene Nguyen, Gavin Yeung, Soheil Ghiasi, Majid Sarrafzadeh {karlene, gavin, soheil, majid}@cs.ucla.edu Abstract We present a framework
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
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
Object Tracking System Using Motion Detection
Object Tracking System Using Motion Detection Harsha K. Ingle*, Prof. Dr. D.S. Bormane** *Department of Electronics and Telecommunication, Pune University, Pune, India Email: harshaingle@gmail.com **Department
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
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
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
Virtual Mouse Implementation using Color Pointer Detection
International Journal of Research Studies in Science, Engineering and Technology Volume 1, Issue 5, August 2014, PP 23-32 ISSN 2349-4751 (Print) & ISSN 2349-476X (Online) Virtual Mouse Implementation using
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
From Product Management Telephone Nuremberg
Release Letter Product: Version: IVA Intelligent Video Analysis 4.50 1. General Intelligent Video Analysis (IVA) version 4.50 is the successor of IVA 4.00. IVA is a continuously growing product with an
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,
International Journal of Advanced Information in Arts, Science & Management Vol.2, No.2, December 2014
Efficient Attendance Management System Using Face Detection and Recognition Arun.A.V, Bhatath.S, Chethan.N, Manmohan.C.M, Hamsaveni M Department of Computer Science and Engineering, Vidya Vardhaka College
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,
The Scientific Data Mining Process
Chapter 4 The Scientific Data Mining Process When I use a word, Humpty Dumpty said, in rather a scornful tone, it means just what I choose it to mean neither more nor less. Lewis Carroll [87, p. 214] In
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
Real Time Target Tracking with Pan Tilt Zoom Camera
2009 Digital Image Computing: Techniques and Applications Real Time Target Tracking with Pan Tilt Zoom Camera Pankaj Kumar, Anthony Dick School of Computer Science The University of Adelaide Adelaide,
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
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
Implementation of Canny Edge Detector of color images on CELL/B.E. Architecture.
Implementation of Canny Edge Detector of color images on CELL/B.E. Architecture. Chirag Gupta,Sumod Mohan K cgupta@clemson.edu, sumodm@clemson.edu Abstract In this project we propose a method to improve
Ultra-scale vehicle tracking in low spatial-resolution and low frame-rate overhead video
LLNL-CONF-413376 Ultra-scale vehicle tracking in low spatial-resolution and low frame-rate overhead video C. J. Carrano May 28, 2009 Signal and Data Processing of Small Targets 2009 in the SPIE Symposium
Urban Vehicle Tracking using a Combined 3D Model Detector and Classifier
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,
Efficient Background Subtraction and Shadow Removal Technique for Multiple Human object Tracking
ISSN: 2321-7782 (Online) Volume 1, Issue 7, December 2013 International Journal of Advance Research in Computer Science and Management Studies Research Paper Available online at: www.ijarcsms.com Efficient
Detection and Restoration of Vertical Non-linear Scratches in Digitized Film Sequences
Detection and Restoration of Vertical Non-linear Scratches in Digitized Film Sequences Byoung-moon You 1, Kyung-tack Jung 2, Sang-kook Kim 2, and Doo-sung Hwang 3 1 L&Y Vision Technologies, Inc., Daejeon,
Tracking of Small Unmanned Aerial Vehicles
Tracking of Small Unmanned Aerial Vehicles Steven Krukowski Adrien Perkins Aeronautics and Astronautics Stanford University Stanford, CA 94305 Email: spk170@stanford.edu Aeronautics and Astronautics Stanford
Chess Vision. Chua Huiyan Le Vinh Wong Lai Kuan
Chess Vision Chua Huiyan Le Vinh Wong Lai Kuan Outline Introduction Background Studies 2D Chess Vision Real-time Board Detection Extraction and Undistortion of Board Board Configuration Recognition 3D
Parallel Ray Tracing using MPI: A Dynamic Load-balancing Approach
Parallel Ray Tracing using MPI: A Dynamic Load-balancing Approach S. M. Ashraful Kadir 1 and Tazrian Khan 2 1 Scientific Computing, Royal Institute of Technology (KTH), Stockholm, Sweden smakadir@csc.kth.se,
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
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
Graphical Processing Units to Accelerate Orthorectification, Atmospheric Correction and Transformations for Big Data
Graphical Processing Units to Accelerate Orthorectification, Atmospheric Correction and Transformations for Big Data Amanda O Connor, Bryan Justice, and A. Thomas Harris IN52A. Big Data in the Geosciences:
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
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
Real-Time Traffic Video Analysis Using Intel Viewmont Coprocessor
Real-Time Traffic Video Analysis Using Intel Viewmont Coprocessor Seon Ho Kim 1, Junyuan Shi 2, Abdullah Alfarrarjeh 3, Daru Xu 2, Yuwei Tan 3, and Cyrus Shahabi 1,2,3 1 Integrated Media Systems Center,
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
OPERATION MANUAL. MV-410RGB Layout Editor. Version 2.1- higher
OPERATION MANUAL MV-410RGB Layout Editor Version 2.1- higher Table of Contents 1. Setup... 1 1-1. Overview... 1 1-2. System Requirements... 1 1-3. Operation Flow... 1 1-4. Installing MV-410RGB Layout
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
Real-Time Background Estimation of Traffic Imagery Using Group-Based Histogram *
JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 24, 411-423 (2008) Real-Time Background Estimation of Traffic Imagery Using Group-Based Histogram KAI-TAI SONG AND JEN-CHAO TAI + Department of Electrical
GPUs for Scientific Computing
GPUs for Scientific Computing p. 1/16 GPUs for Scientific Computing Mike Giles mike.giles@maths.ox.ac.uk Oxford-Man Institute of Quantitative Finance Oxford University Mathematical Institute Oxford e-research
Medical Image Segmentation of PACS System Image Post-processing *
Medical Image Segmentation of PACS System Image Post-processing * Lv Jie, Xiong Chun-rong, and Xie Miao Department of Professional Technical Institute, Yulin Normal University, Yulin Guangxi 537000, China
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
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,
Real-time Visual Tracker by Stream Processing
Real-time Visual Tracker by Stream Processing Simultaneous and Fast 3D Tracking of Multiple Faces in Video Sequences by Using a Particle Filter Oscar Mateo Lozano & Kuzahiro Otsuka presented by Piotr Rudol
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
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
An Implementation Of Multiprocessor Linux
An Implementation Of Multiprocessor Linux This document describes the implementation of a simple SMP Linux kernel extension and how to use this to develop SMP Linux kernels for architectures other than
Intel Processor Pricing Effective 4/20/2008 1Ku Tray Units
Desktop Intel Core 2 Extreme Desktop Processor Intel Core 2 Extreme Processor QX9775 (12M Cache, 3.20 GHz, 1600 MHz FSB) Intel Core 2 Extreme Processor QX9770 (12M Cache, 3.20 GHz, 1600 MHz FSB) Intel
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
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
Object tracking & Motion detection in video sequences
Introduction Object tracking & Motion detection in video sequences Recomended link: http://cmp.felk.cvut.cz/~hlavac/teachpresen/17compvision3d/41imagemotion.pdf 1 2 DYNAMIC SCENE ANALYSIS The input to
Journal of Industrial Engineering Research. Adaptive sequence of Key Pose Detection for Human Action Recognition
IWNEST PUBLISHER Journal of Industrial Engineering Research (ISSN: 2077-4559) Journal home page: http://www.iwnest.com/aace/ Adaptive sequence of Key Pose Detection for Human Action Recognition 1 T. Sindhu
Using MATLAB to Measure the Diameter of an Object within an Image
Using MATLAB to Measure the Diameter of an Object within an Image Keywords: MATLAB, Diameter, Image, Measure, Image Processing Toolbox Author: Matthew Wesolowski Date: November 14 th 2014 Executive Summary
Serious 4K Editing. Hardware Performance Guide
Hardware Performance Guide 4K Video Editing Intel Xeon Processor Workstations Hardware Performance Guide Serious 4K Editing For today s content creators, one of the biggest market trends across the entertainment
2 Pelco Video Analytics
Solution Video Analytics: Title: Key Extending Words Book Security Rest With in Light Technology Few new technologies have excited the imagination of video security professionals quite like intelligent
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
Integer Computation of Image Orthorectification for High Speed Throughput
Integer Computation of Image Orthorectification for High Speed Throughput Paul Sundlie Joseph French Eric Balster Abstract This paper presents an integer-based approach to the orthorectification of aerial
Interactive Flag Identification using Image Retrieval Techniques
Interactive Flag Identification using Image Retrieval Techniques Eduardo Hart, Sung-Hyuk Cha, Charles Tappert CSIS, Pace University 861 Bedford Road, Pleasantville NY 10570 USA E-mail: eh39914n@pace.edu,
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
Cloud Resource Management for Image and Video Analysis of Big Data from Network Cameras
-RXIVREXMSREP SRJIVIRGISR PSYH SQTYXMRKERH&MK(EXE Cloud Resource Management for Image and Video Analysis of Big Data from Network Cameras Ahmed S. Kaseb, Anup Mohan, Yung-Hsiang Lu School of Electrical
Honeywell Video Analytics
INTELLIGENT VIDEO ANALYTICS Honeywell s suite of video analytics products enables enhanced security and surveillance solutions by automatically monitoring video for specific people, vehicles, objects and
A Computer Vision System for Monitoring Production of Fast Food
ACCV2002: The 5th Asian Conference on Computer Vision, 23 25 January 2002, Melbourne, Australia A Computer Vision System for Monitoring Production of Fast Food Richard Russo Mubarak Shah Niels Lobo Computer
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
ROBUST REAL-TIME ON-BOARD VEHICLE TRACKING SYSTEM USING PARTICLES FILTER. Ecole des Mines de Paris, Paris, France
ROBUST REAL-TIME ON-BOARD VEHICLE TRACKING SYSTEM USING PARTICLES FILTER Bruno Steux Yotam Abramson Ecole des Mines de Paris, Paris, France Abstract: We describe a system for detection and tracking of
Pre-Emptive, Economic Security for Perimeters & Outdoor Areas
WHITE PAPER Pre-Emptive, Economic Security for Perimeters & Outdoor Areas Without reliable detection, an outdoor security system cannot be trusted. Excessive false alarms waste manpower and fail to command
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
Comparing Improved Versions of K-Means and Subtractive Clustering in a Tracking Application
Comparing Improved Versions of K-Means and Subtractive Clustering in a Tracking Application Marta Marrón Romera, Miguel Angel Sotelo Vázquez, and Juan Carlos García García Electronics Department, University
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
Scalability and Classifications
Scalability and Classifications 1 Types of Parallel Computers MIMD and SIMD classifications shared and distributed memory multicomputers distributed shared memory computers 2 Network Topologies static
Colour Image Segmentation Technique for Screen Printing
60 R.U. Hewage and D.U.J. Sonnadara Department of Physics, University of Colombo, Sri Lanka ABSTRACT Screen-printing is an industry with a large number of applications ranging from printing mobile phone
A Study on M2M-based AR Multiple Objects Loading Technology using PPHT
A Study on M2M-based AR Multiple Objects Loading Technology using PPHT Sungmo Jung, Seoksoo Kim * Department of Multimedia Hannam University 133, Ojeong-dong, Daedeok-gu, Daejeon-city Korea sungmoj@gmail.com,
Interactive Flag Identification Using a Fuzzy-Neural Technique
Proceedings of Student/Faculty Research Day, CSIS, Pace University, May 7th, 2004 Interactive Flag Identification Using a Fuzzy-Neural Technique 1. Introduction Eduardo art, Sung-yuk Cha, Charles Tappert
Open Source Computer Vision and Analytics in Support of the UAS and ASPRS Communities
Open Source Computer Vision and Analytics in Support of the UAS and ASPRS Communities Heather James, Manager Innovative Applications Computer Vision Group Heather.james@kitware.com (518) 290-6576 Best
Enabling Technologies for Distributed and Cloud Computing
Enabling Technologies for Distributed and Cloud Computing Dr. Sanjay P. Ahuja, Ph.D. 2010-14 FIS Distinguished Professor of Computer Science School of Computing, UNF Multi-core CPUs and Multithreading
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
3 Image-Based Photo Hulls. 2 Image-Based Visual Hulls. 3.1 Approach. 3.2 Photo-Consistency. Figure 1. View-dependent geometry.
Image-Based Photo Hulls Greg Slabaugh, Ron Schafer Georgia Institute of Technology Center for Signal and Image Processing Atlanta, GA 30332 {slabaugh, rws}@ece.gatech.edu Mat Hans Hewlett-Packard Laboratories
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
Interpreting hand-over-face gestures
Interpreting hand-over-face gestures Marwa Mahmoud and Peter Robinson University of Cambridge Abstract. People often hold their hands near their faces as a gesture in natural conversation, which can interfere
Vehicle Tracking Using On-Line Fusion of Color and Shape Features
Vehicle Tracking Using On-Line Fusion of Color and Shape Features Kai She 1, George Bebis 1, Haisong Gu 1, and Ronald Miller 2 1 Computer Vision Laboratory, University of Nevada, Reno, NV 2 Vehicle Design
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
Achieving Nanosecond Latency Between Applications with IPC Shared Memory Messaging
Achieving Nanosecond Latency Between Applications with IPC Shared Memory Messaging In some markets and scenarios where competitive advantage is all about speed, speed is measured in micro- and even nano-seconds.
A Noble Integrated Management System based on Mobile and Cloud service for preventing various hazards
, pp.166-171 http://dx.doi.org/10.14257/astl.205.98.42 A Noble Integrated Management System based on Mobile and Cloud service for preventing various hazards Yeo ChangSub 1, Ryu HyunKi 1 and Lee HaengSuk
Vocera Voice 4.3 and 4.4 Server Sizing Matrix
Vocera Voice 4.3 and 4.4 Server Sizing Matrix Vocera Server Recommended Configuration Guidelines Maximum Simultaneous Users 450 5,000 Sites Single Site or Multiple Sites Requires Multiple Sites Entities
Boundless Security Systems, Inc.
Boundless Security Systems, Inc. sharper images with better access and easier installation Product Overview Product Summary Data Sheet Control Panel client live and recorded viewing, and search software
Observing Human Behavior in Image Sequences: the Video Hermeneutics Challenge
Observing Human Behavior in Image Sequences: the Video Hermeneutics Challenge Pau Baiget, Jordi Gonzàlez Computer Vision Center, Dept. de Ciències de la Computació, Edifici O, Campus UAB, 08193 Bellaterra,
Stream Processing on GPUs Using Distributed Multimedia Middleware
Stream Processing on GPUs Using Distributed Multimedia Middleware Michael Repplinger 1,2, and Philipp Slusallek 1,2 1 Computer Graphics Lab, Saarland University, Saarbrücken, Germany 2 German Research
Real-time processing the basis for PC Control
Beckhoff real-time kernels for DOS, Windows, Embedded OS and multi-core CPUs Real-time processing the basis for PC Control Beckhoff employs Microsoft operating systems for its PCbased control technology.
A Color Hand Gesture Database for Evaluating and Improving Algorithms on Hand Gesture and Posture Recognition
Res. Lett. Inf. Math. Sci., 2005, Vol. 7, pp 127-134 127 Available online at http://iims.massey.ac.nz/research/letters/ A Color Hand Gesture Database for Evaluating and Improving Algorithms on Hand Gesture
FACE RECOGNITION BASED ATTENDANCE MARKING SYSTEM
Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 3, Issue. 2, February 2014,
Graphical Processing Units to Accelerate Orthorectification, Atmospheric Correction and Transformations for Big Data
Graphical Processing Units to Accelerate Orthorectification, Atmospheric Correction and Transformations for Big Data Amanda O Connor, Bryan Justice, and A. Thomas Harris IN52A. Big Data in the Geosciences:
Initial Hardware Estimation Guidelines. AgilePoint BPMS v5.0 SP1
Initial Hardware Estimation Guidelines Document Revision r5.2.3 November 2011 Contents 2 Contents Preface...3 Disclaimer of Warranty...3 Copyright...3 Trademarks...3 Government Rights Legend...3 Virus-free