Human behavior analysis from videos using optical flow

Size: px
Start display at page:

Download "Human behavior analysis from videos using optical flow"

Transcription

1 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 Workshop 2011 UNIVERSITE DES SCIENCES ET TECHNOLGIES DE LILLE LIFL UMR 8022 Bât. M Villeneuve d Ascq cedex Tél. : (33) Fax : (33)

2 Introduction State of the Art Global approach Plan Recognition of human Actions Crowd Event Detection Motion Pattern Extraction Conclusion 2

3 Introduction Automatic behavior analysis is a very active field in research and industry It consists in extracting information from videos using computer vision algorithms The extracted information is used to: Assist surveillance operators Provide statistics for marketing agents Perform video retrieval Allow more natural and immersive human machine interactions etc 3

4 State of the art Many approaches have been proposed for behavior analysis Human activity recognition [Le et al. cvpr2011 ] Crowd event detection [Adam et al. TPAMI 2008] Motion pattern extraction [Rodriguez et al, iccv2009] However, they were focusing on a single aspect of behavior analysis or were very complex Example : Dynamic textures [Ma and Cisar, cvpr2009] Privacy issues are not addressed Intelligent cameras that contain embedded software require fast and reusable algorithms 4

5 Our approach We propose a generic approach for behavior analysis It is based on three levels of features Easier understanding Each level can be designed separately More control Each level can be reused for other purposes Save more processing power The lower level relies on motion information Preserves privacy out of the box 5

6 General Approach Applications Human action recognition Crowd event detection Motion pattern extraction High level information Mid-level descriptors Low level features Video stream 6

7 General approach LOW LEVEL FEATURES 7

8 Interest point detection Identification of good points that can be efficiently and easily tracked. We used the «good features to track» algorithm Fast and efficient OpenCV implementation Jianbo Shi; Tomasi, C.;, "Good features to track," Computer Vision and Pattern Recognition, Proceedings CVPR '94., 1994 IEEE Computer Society Conference on, vol., no., pp , Jun 1994 doi: /CVPR

9 Optical flow computation Estimate the motion of interest points Implementation of Bouguet + = Frame t and its interest points Frame t+1 Optical flow vectors 9

10 General Approach Applications Human action recognition Crowd event detection Motion pattern extraction High level information Mid-level descriptors Low level features Video stream 10

11 General approach MID-LEVEL FEATURES : DIRECTION MODEL AND MAGNITUDE MODEL 11

12 Vector allocation to blocks Each vector is allocated to a block depending on its origin Eliminate vectors with a very small or a very big magnitude Optical flow vectors allocated to a matrix of 8x4 blocs 12

13 Direction model The orientations of optical flow vectors are clustered in each bloc The circular data is clustered using von Mises distributions 13

14 Direction model The orientations of optical flow vectors are clustered in each bloc The circular data is clustered using von Mises distributions 14

15 Direction model (2) The direction model is updated at each new frame for all the duration of the video clip t=0 Optical flow Direction model 15

16 Direction model (2) The direction model is updated at each new frame for all the duration of the video clip t=40 Optical flow Direction model Bloc size: 20x20 16

17 Direction model (2) The direction model is updated at each new frame for all the duration of the video clip T=115 Optical flow Direction model Bloc size: 20x20 17

18 Direction model (2) The direction model is updated at each new frame for all the duration of the video clip T=160 Optical flow Direction model Bloc size: 20x20 18

19 Magnitude model The magnitude model is estimated following the same steps as the direction model We estimate a Gaussian mixture for each bloc 19

20 General approach APPLICATIONS 20

21 General Approach Applications Human action recognition Crowd event detection Motion pattern extraction High level information Mid-level descriptors Low level features Video stream 21

22 Human Action Recognition Different terminologies (action, activity, event) In this presentation: action recogntion consists in the identification of simple daylife actions(ex : walk, run...) Our input is a video (query video) captured from a monocular camera Answer to the phone Boxing 22

23 Model associated to a video sequence Model of a video = (direction model, magnitude model) 23

24 Distance metric walking running jogging Query model handwaving handclapping boxing Template models 24

25 Distance metric walking running jogging Query model handwaving handclapping boxing Template models 25

26 Distance metric Detected event walking running jogging Query model handwaving handclapping boxing Template models 26

27 Distance metric Distance between two direction models Distance between two magnitude models 27

28 Result comparison KTH dataset ADL dataset [BALD11] Yassine Benabbas, Samir Amir, Adel Lablack, and Chabane Djeraba. Human action recognition using direction and magnitude models of motion. In International Conference on Computer Vision and Applications (VISAPP),

29 General Approach Applications Human action recognition Crowd event detection Motion pattern extraction High level information Mid-level descriptors Low level features Video stream 29

30 Crowd Event Detection Objective: Detection of interesting events or situation that occur in a crowd scene The targeted events are: Running Splitting Local Dispersion Evacuation Merging These events are defined in the PETS 2009 workshop. 30

31 Compute the instantaneous direction model Compute the direction model for the current frame Keep only the main orientation for each block of the direction model 31

32 Group Clustering and Tracking Cluster the neighboring blocks that have a similar direction into a group. 32

33 Group Clustering and Tracking Cluster the neighboring blocks that have a similar direction into a group. 33

34 Group Clustering and Tracking Cluster the neighboring blocks that have a similar direction into a group. 34

35 Group Clustering and Tracking Cluster the neighboring blocks that have a similar direction into a group. Define an orientation and a centroid for each group. Each group is tracked over the next frames 35

36 We use two classifiers: Event detection One for running and walking events using the mean motion speed as a feature One for local dispersion, split, merge and evacuation events using as features: Number of groups Mean orientation The circular variance Mean motion speed The mean distance between groups Using two classifiers allows to detect 36

37 Comparison [BID11] - Yassine Benabbas, Nacim Ihaddadene, and Chabane Djeraba. Motion pattern extraction and event detection for automatic visual surveillance. EURASIP Journal on Image and Video Processing, 2011:15,

38 General Approach Applications Human action recognition Crowd event detection Motion pattern extraction High level information Mid-level descriptors Low level features Video stream 38

39 Motion Pattern Extraction It consists of extracting usual (or repetitive) patterns (or trends) of motion It can be considered as a synthesized information about the motion behavior in a video 39

40 Motion Pattern Extraction Motions patterns learned from a given scene can be used for modeling usual behaviors of subjects and have a lot of applications: They provide relevant information about subjects behavior. They can improve tracking results. They can help to detect events. Learning motion patterns in unstructured crowd scenes is a difficult task; In some locations in the scene, the motion has different orientations (example : zebra crossing) 40

41 Clustering similar regions Affect at most k major orientations for each cell. They are obtained from the cell s mixture model. A direction model is obtained Representation of the learned direction model 41

42 Clustering similar regions Cluster similar blocks depending on their major orientations Two blocks are similar If they are neighbor, the window is one block. And the cosine similarity between two of their major orientations is less that a predefined threshold. A block can belong to a maximum of k clusters Direction Model Pattern 1 Pattern 2 Pattern 3 42

43 Experiments Car traffic video from the AVSS dataset The orientations of optical flow vectors are represented 43

44 Detected patterns 44

45 Putting it all together 45

46 Escalator 46

47 Comparison [BID11] - Yassine Benabbas, Nacim Ihaddadene, and Chabane Djeraba. Motion pattern extraction and event detection for automatic visual surveillance. EURASIP Journal on Image and Video Processing, 2011:15,

48 Conclusion and future works Conclusions General approach for video analysis Based on motion, which preserves privacy Very promising results Can be easily improved and applied to other applications Future works Open source behavior analysis toolbox Apply approaches in real environments Scale independent features In event detection: apply weights to direction and magnitude models Affine group analysis (detect walking and running persons inside a group) 48

49 Thank you for your attention QUESTIONS? 49

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

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

Face Recognition using SIFT Features

Face Recognition using SIFT Features Face Recognition using SIFT Features Mohamed Aly CNS186 Term Project Winter 2006 Abstract Face recognition has many important practical applications, like surveillance and access control.

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

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

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

Journal of Industrial Engineering Research. Adaptive sequence of Key Pose Detection for Human Action Recognition

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

More information

Background Subtraction

Background Subtraction Background Subtraction Birgi Tamersoy The University of Texas at Austin September 29 th, 2009 Background Subtraction Given an image (mostly likely to be a video frame), we want to identify the foreground

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

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

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

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

Feature Tracking and Optical Flow

Feature Tracking and Optical Flow 02/09/12 Feature Tracking and Optical Flow Computer Vision CS 543 / ECE 549 University of Illinois Derek Hoiem Many slides adapted from Lana Lazebnik, Silvio Saverse, who in turn adapted slides from Steve

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

Volume 3, Issue 6, June 2015 International Journal of Advance Research in Computer Science and Management Studies

Volume 3, Issue 6, June 2015 International Journal of Advance Research in Computer Science and Management Studies Volume 3, Issue 6, June 2015 International Journal of Advance Research in Computer Science and Management Studies Research Article / Survey Paper / Case Study Available online at: www.ijarcsms.com Image

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

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

Multiple Object Tracking Using SIFT Features and Location Matching

Multiple Object Tracking Using SIFT Features and Location Matching Multiple Object Tracking Using SIFT Features and Location Matching Seok-Wun Ha 1, Yong-Ho Moon 2 1,2 Dept. of Informatics, Engineering Research Institute, Gyeongsang National University, 900 Gazwa-Dong,

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

A Method of Caption Detection in News Video

A Method of Caption Detection in News Video 3rd International Conference on Multimedia Technology(ICMT 3) A Method of Caption Detection in News Video He HUANG, Ping SHI Abstract. News video is one of the most important media for people to get information.

More information

Video-based Animal Behavior Analysis From Multiple Cameras

Video-based Animal Behavior Analysis From Multiple Cameras Video-based Animal Behavior Analysis From Multiple Cameras Xinwei Xue and Thomas C. Henderson Abstract It has become increasingly popular to study animal behaviors with the assistance of video recordings.

More information

A General Framework for Tracking Objects in a Multi-Camera Environment

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

More information

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

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,

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

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

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

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

Edge tracking for motion segmentation and depth ordering

Edge tracking for motion segmentation and depth ordering Edge tracking for motion segmentation and depth ordering P. Smith, T. Drummond and R. Cipolla Department of Engineering University of Cambridge Cambridge CB2 1PZ,UK {pas1001 twd20 cipolla}@eng.cam.ac.uk

More information

Online Learning for Fast Segmentation of Moving Objects

Online Learning for Fast Segmentation of Moving Objects Online Learning for Fast Segmentation of Moving Objects Liam Ellis, Vasileios Zografos {liam.ellis,vasileios.zografos}@liu.se CVL, Linköping University, Linköping, Sweden Abstract. This work addresses

More information

Fast Matching of Binary Features

Fast Matching of Binary Features Fast Matching of Binary Features Marius Muja and David G. Lowe Laboratory for Computational Intelligence University of British Columbia, Vancouver, Canada {mariusm,lowe}@cs.ubc.ca Abstract There has been

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

The Scientific Data Mining Process

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

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

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

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 Computer Vision System for Monitoring Production of Fast Food

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

More information

LIBSVX and Video Segmentation Evaluation

LIBSVX and Video Segmentation Evaluation CVPR 14 Tutorial! 1! LIBSVX and Video Segmentation Evaluation Chenliang Xu and Jason J. Corso!! Computer Science and Engineering! SUNY at Buffalo!! Electrical Engineering and Computer Science! University

More information

Tracking of Multiple Objects under Partial Occlusion

Tracking of Multiple Objects under Partial Occlusion Tracking of Multiple Objects under Partial Occlusion Bing Han, Christopher Paulson, Taoran Lu, Dapeng Wu, Jian Li Department of Electrical and Computer Engineering University of Florida Gainesville, FL

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

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

Laser Gesture Recognition for Human Machine Interaction

Laser Gesture Recognition for Human Machine Interaction International Journal of Computer Sciences and Engineering Open Access Research Paper Volume-04, Issue-04 E-ISSN: 2347-2693 Laser Gesture Recognition for Human Machine Interaction Umang Keniya 1*, Sarthak

More information

CHAPTER 6 TEXTURE ANIMATION

CHAPTER 6 TEXTURE ANIMATION CHAPTER 6 TEXTURE ANIMATION 6.1. INTRODUCTION Animation is the creating of a timed sequence or series of graphic images or frames together to give the appearance of continuous movement. A collection of

More information

Detecting and Tracking Moving Objects for Video Surveillance

Detecting and Tracking Moving Objects for Video Surveillance IEEE Proc. Computer Vision and Pattern Recognition Jun. 3-5, 1999. Fort Collins CO Detecting and Tracking Moving Objects for Video Surveillance Isaac Cohen Gérard Medioni University of Southern California

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

ROBUST VEHICLE TRACKING IN VIDEO IMAGES BEING TAKEN FROM A HELICOPTER

ROBUST VEHICLE TRACKING IN VIDEO IMAGES BEING TAKEN FROM A HELICOPTER ROBUST VEHICLE TRACKING IN VIDEO IMAGES BEING TAKEN FROM A HELICOPTER Fatemeh Karimi Nejadasl, Ben G.H. Gorte, and Serge P. Hoogendoorn Institute of Earth Observation and Space System, Delft University

More information

Introduction. Selim Aksoy. Bilkent University saksoy@cs.bilkent.edu.tr

Introduction. Selim Aksoy. Bilkent University saksoy@cs.bilkent.edu.tr Introduction Selim Aksoy Department of Computer Engineering Bilkent University saksoy@cs.bilkent.edu.tr What is computer vision? What does it mean, to see? The plain man's answer (and Aristotle's, too)

More information

Tracking Moving Objects In Video Sequences Yiwei Wang, Robert E. Van Dyck, and John F. Doherty Department of Electrical Engineering The Pennsylvania State University University Park, PA16802 Abstract{Object

More information

An Active Head Tracking System for Distance Education and Videoconferencing Applications

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

More information

Practical Tour of Visual tracking. David Fleet and Allan Jepson January, 2006

Practical Tour of Visual tracking. David Fleet and Allan Jepson January, 2006 Practical Tour of Visual tracking David Fleet and Allan Jepson January, 2006 Designing a Visual Tracker: What is the state? pose and motion (position, velocity, acceleration, ) shape (size, deformation,

More information

Efficient visual search of local features. Cordelia Schmid

Efficient visual search of local features. Cordelia Schmid Efficient visual search of local features Cordelia Schmid Visual search change in viewing angle Matches 22 correct matches Image search system for large datasets Large image dataset (one million images

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

Robust Real-Time Face Tracking Using an Active Camera

Robust Real-Time Face Tracking Using an Active Camera Robust Real-Time Face Tracking Using an Active Camera Paramveer S. Dhillon CIS Department, University of Pennsylvania, Philadelphia, PA 19104, U.S.A Abstract. This paper addresses the problem of facial

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

Object tracking & Motion detection in video sequences

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

More information

Product Characteristics Page 2. Management & Administration Page 2. Real-Time Detections & Alerts Page 4. Video Search Page 6

Product Characteristics Page 2. Management & Administration Page 2. Real-Time Detections & Alerts Page 4. Video Search Page 6 Data Sheet savvi Version 5.3 savvi TM is a unified video analytics software solution that offers a wide variety of analytics functionalities through a single, easy to use platform that integrates with

More information

Open-Set Face Recognition-based Visitor Interface System

Open-Set Face Recognition-based Visitor Interface System Open-Set Face Recognition-based Visitor Interface System Hazım K. Ekenel, Lorant Szasz-Toth, and Rainer Stiefelhagen Computer Science Department, Universität Karlsruhe (TH) Am Fasanengarten 5, Karlsruhe

More information

Real-time Traffic Congestion Detection Based on Video Analysis

Real-time Traffic Congestion Detection Based on Video Analysis Journal of Information & Computational Science 9: 10 (2012) 2907 2914 Available at http://www.joics.com Real-time Traffic Congestion Detection Based on Video Analysis Shan Hu a,, Jiansheng Wu a, Ling Xu

More information

Fall detection in the elderly by head tracking

Fall detection in the elderly by head tracking Loughborough University Institutional Repository Fall detection in the elderly by head tracking This item was submitted to Loughborough University's Institutional Repository by the/an author. Citation:

More information

Speaker Change Detection using Support Vector Machines

Speaker Change Detection using Support Vector Machines Speaker Change Detection using Support Vector Machines V. Kartik and D. Srikrishna Satish and C. Chandra Sekhar Speech and Vision Laboratory Department of Computer Science and Engineering Indian Institute

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

Circle Object Recognition Based on Monocular Vision for Home Security Robot

Circle Object Recognition Based on Monocular Vision for Home Security Robot Journal of Applied Science and Engineering, Vol. 16, No. 3, pp. 261 268 (2013) DOI: 10.6180/jase.2013.16.3.05 Circle Object Recognition Based on Monocular Vision for Home Security Robot Shih-An Li, Ching-Chang

More information

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. 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

More information

The Big Data methodology in computer vision systems

The Big Data methodology in computer vision systems The Big Data methodology in computer vision systems Popov S.B. Samara State Aerospace University, Image Processing Systems Institute, Russian Academy of Sciences Abstract. I consider the advantages of

More information

LOCAL SURFACE PATCH BASED TIME ATTENDANCE SYSTEM USING FACE. indhubatchvsa@gmail.com

LOCAL SURFACE PATCH BASED TIME ATTENDANCE SYSTEM USING FACE. indhubatchvsa@gmail.com LOCAL SURFACE PATCH BASED TIME ATTENDANCE SYSTEM USING FACE 1 S.Manikandan, 2 S.Abirami, 2 R.Indumathi, 2 R.Nandhini, 2 T.Nanthini 1 Assistant Professor, VSA group of institution, Salem. 2 BE(ECE), VSA

More information

In: Proceedings of RECPAD 2002-12th Portuguese Conference on Pattern Recognition June 27th- 28th, 2002 Aveiro, Portugal

In: Proceedings of RECPAD 2002-12th Portuguese Conference on Pattern Recognition June 27th- 28th, 2002 Aveiro, Portugal Paper Title: Generic Framework for Video Analysis Authors: Luís Filipe Tavares INESC Porto lft@inescporto.pt Luís Teixeira INESC Porto, Universidade Católica Portuguesa lmt@inescporto.pt Luís Corte-Real

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

Motion Interaction Field for Accident Detection in Traffic Surveillance Video

Motion Interaction Field for Accident Detection in Traffic Surveillance Video 2 22nd International Conference on Pattern Recognition Motion Interaction Field for Accident Detection in Traffic Surveillance Video Kimin Yun, Hawook Jeong, Kwang Moo Yi, Soo Wan Kim and Jin Young Choi

More information

REAL TIME TRAFFIC LIGHT CONTROL USING IMAGE PROCESSING

REAL TIME TRAFFIC LIGHT CONTROL USING IMAGE PROCESSING REAL TIME TRAFFIC LIGHT CONTROL USING IMAGE PROCESSING Ms.PALLAVI CHOUDEKAR Ajay Kumar Garg Engineering College, Department of electrical and electronics Ms.SAYANTI BANERJEE Ajay Kumar Garg Engineering

More information

Local features and matching. Image classification & object localization

Local features and matching. Image classification & object localization Overview Instance level search Local features and matching Efficient visual recognition Image classification & object localization Category recognition Image classification: assigning a class label to

More information

Virtual Mouse Implementation using Color Pointer Detection

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

More information

Solving Big Data Problems in Computer Vision with MATLAB Loren Shure

Solving Big Data Problems in Computer Vision with MATLAB Loren Shure Solving Big Data Problems in Computer Vision with MATLAB Loren Shure 2015 The MathWorks, Inc. 1 Why Are We Talking About Big Data? 100 hours of video uploaded to YouTube per minute 1 Explosive increase

More information

CS229 Project Final Report. Sign Language Gesture Recognition with Unsupervised Feature Learning

CS229 Project Final Report. Sign Language Gesture Recognition with Unsupervised Feature Learning CS229 Project Final Report Sign Language Gesture Recognition with Unsupervised Feature Learning Justin K. Chen, Debabrata Sengupta, Rukmani Ravi Sundaram 1. Introduction The problem we are investigating

More information

Scalable Traffic Video Analytics using Hadoop MapReduce

Scalable Traffic Video Analytics using Hadoop MapReduce Scalable Traffic Video Analytics using Hadoop MapReduce Vaithilingam Anantha Natarajan Subbaiyan Jothilakshmi Venkat N Gudivada Department of Computer Science and Engineering Annamalai University Tamilnadu,

More information

Modelling, Extraction and Description of Intrinsic Cues of High Resolution Satellite Images: Independent Component Analysis based approaches

Modelling, Extraction and Description of Intrinsic Cues of High Resolution Satellite Images: Independent Component Analysis based approaches Modelling, Extraction and Description of Intrinsic Cues of High Resolution Satellite Images: Independent Component Analysis based approaches PhD Thesis by Payam Birjandi Director: Prof. Mihai Datcu Problematic

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

Movie Classification Using k-means and Hierarchical Clustering

Movie Classification Using k-means and Hierarchical Clustering Movie Classification Using k-means and Hierarchical Clustering An analysis of clustering algorithms on movie scripts Dharak Shah DA-IICT, Gandhinagar Gujarat, India dharak_shah@daiict.ac.in Saheb Motiani

More information

Mobile Video Capture of Multi-Page Documents

Mobile Video Capture of Multi-Page Documents Mobile Video Capture of Multi-Page Documents Jayant Kumar (UMD College Park) Raja Bala, Hengzhou Ding, Phillip Emmett (Xerox Research Center Webster) International Workshop on Mobile Vision June 23, 2013

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

Using Data Mining for Mobile Communication Clustering and Characterization

Using Data Mining for Mobile Communication Clustering and Characterization Using Data Mining for Mobile Communication Clustering and Characterization A. Bascacov *, C. Cernazanu ** and M. Marcu ** * Lasting Software, Timisoara, Romania ** Politehnica University of Timisoara/Computer

More information

Alex Leykin. Visual people tracking and group activity recognition. Visual attention, behavior and body language analysis with non-intrusive sensors.

Alex Leykin. Visual people tracking and group activity recognition. Visual attention, behavior and body language analysis with non-intrusive sensors. Alex Leykin Tel: 812-219-6672 oleykin@indiana.edu http://cgi.cs.indiana.edu/~oleykin Education 2007 PhD in Computer Science, Indiana University 2002 MS in Computer Science, Indiana University 2000 MS in

More information

Human Face Detection in Cluttered Color Images Using Skin Color and Edge Information

Human Face Detection in Cluttered Color Images Using Skin Color and Edge Information Human Face Detection in Cluttered Color Images Using Skin Color and Edge Information K. Sandeep and A.N. Rajagopalan Department of Electrical Engineering Indian Institute of Technology Madras Chennai 600

More information

Segmentation of building models from dense 3D point-clouds

Segmentation of building models from dense 3D point-clouds Segmentation of building models from dense 3D point-clouds Joachim Bauer, Konrad Karner, Konrad Schindler, Andreas Klaus, Christopher Zach VRVis Research Center for Virtual Reality and Visualization, Institute

More information

BEYOND PARTICLE FLOW: BAG OF TRAJECTORY GRAPHS FOR DENSE CROWD EVENT RECOGNITION

BEYOND PARTICLE FLOW: BAG OF TRAJECTORY GRAPHS FOR DENSE CROWD EVENT RECOGNITION BEYOND PARTICLE FLOW: BAG OF TRAJECTORY GRAPHS FOR DENSE CROWD EVENT RECOGNITION Yanhao Zhang, Lei Qin, Hongxun Yao, Pengfei Xu, Qingming Huang Harbin Institute of Technology, Harbin, 150001, China Inst.

More information

Interactive Flag Identification using Image Retrieval Techniques

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,

More information

A Study on SURF Algorithm and Real-Time Tracking Objects Using Optical Flow

A Study on SURF Algorithm and Real-Time Tracking Objects Using Optical Flow , pp.233-237 http://dx.doi.org/10.14257/astl.2014.51.53 A Study on SURF Algorithm and Real-Time Tracking Objects Using Optical Flow Giwoo Kim 1, Hye-Youn Lim 1 and Dae-Seong Kang 1, 1 Department of electronices

More information

Removing Moving Objects from Point Cloud Scenes

Removing Moving Objects from Point Cloud Scenes 1 Removing Moving Objects from Point Cloud Scenes Krystof Litomisky klitomis@cs.ucr.edu Abstract. Three-dimensional simultaneous localization and mapping is a topic of significant interest in the research

More information

5 Performance Management for Web Services. Rolf Stadler School of Electrical Engineering KTH Royal Institute of Technology. stadler@ee.kth.

5 Performance Management for Web Services. Rolf Stadler School of Electrical Engineering KTH Royal Institute of Technology. stadler@ee.kth. 5 Performance Management for Web Services Rolf Stadler School of Electrical Engineering KTH Royal Institute of Technology stadler@ee.kth.se April 2008 Overview Service Management Performance Mgt QoS Mgt

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

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

Youtube. Mining Specific Actions from Youtube Video with Spatio-Temporal Features

Youtube. Mining Specific Actions from Youtube Video with Spatio-Temporal Features THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. Youtube DOHANG NGA 182 8585 1 5 1 E-mail: {dohang,yanai}@mm.cs.uec.ac.jp Web Web Youtube Web2.0 bag-of-features

More information

Research Statement: Towards Detailed Recognition of Visual Categories

Research Statement: Towards Detailed Recognition of Visual Categories As humans, we have a remarkable ability to perceive the world around us in minute detail purely from the light that is reflected off it we can estimate material and metric properties of objects, localize

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

Extraction of Moving Objects Using Frame Differencing, Ghost and Shadow Removal

Extraction of Moving Objects Using Frame Differencing, Ghost and Shadow Removal 2014 Fifth International Conference on Intelligent Systems, Modelling and Simulation Extraction of Moving Objects Using Frame Differencing, Ghost and Shadow Removal Syaimaa Solehah Mohd Radzi 1, Shahrul

More information

Keywords Gaussian probability, YCrCb,RGB Model

Keywords Gaussian probability, YCrCb,RGB Model Volume 4, Issue 7, July 2014 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Skin Segmentation

More information

Human Behavior Analysis in Intelligent Retail Environments

Human Behavior Analysis in Intelligent Retail Environments Human Behavior Analysis in Intelligent Retail Environments Andrea Ascani, Emanuele Frontoni, Adriano Mancini, Primo Zingaretti 1 D.I.I.G.A., Università Politecnica delle Marche, Ancona - Italy, {ascani,

More information

Using Microsoft Kinect Sensor in Our Research

Using Microsoft Kinect Sensor in Our Research Using Microsoft Kinect Sensor in Our Research Hao Zhang Distributed Intelligence Laboratory Dept. of Electrical Engineering and Computer Science University of Tennessee, Knoxville Sep. 20, 2011 Outline

More information

Cloud tracking with optical flow for short-term solar forecasting

Cloud tracking with optical flow for short-term solar forecasting Cloud tracking with optical flow for short-term solar forecasting Philip Wood-Bradley, José Zapata, John Pye Solar Thermal Group, Australian National University, Canberra, Australia Corresponding author:

More information

Speaker: Prof. Mubarak Shah, University of Central Florida. Title: Representing Human Actions as Motion Patterns

Speaker: Prof. Mubarak Shah, University of Central Florida. Title: Representing Human Actions as Motion Patterns Speaker: Prof. Mubarak Shah, University of Central Florida Title: Representing Human Actions as Motion Patterns Abstract: Automatic analysis of videos is one of most challenging problems in Computer vision.

More information

A Cognitive Approach to Vision for a Mobile Robot

A Cognitive Approach to Vision for a Mobile Robot A Cognitive Approach to Vision for a Mobile Robot D. Paul Benjamin Christopher Funk Pace University, 1 Pace Plaza, New York, New York 10038, 212-346-1012 benjamin@pace.edu Damian Lyons Fordham University,

More information

Calibrating a Camera and Rebuilding a Scene by Detecting a Fixed Size Common Object in an Image

Calibrating a Camera and Rebuilding a Scene by Detecting a Fixed Size Common Object in an Image Calibrating a Camera and Rebuilding a Scene by Detecting a Fixed Size Common Object in an Image Levi Franklin Section 1: Introduction One of the difficulties of trying to determine information about a

More information