Building a Multiple Object Tracking System with Occlusion Handling in Surveillance Videos

Video tracking systems are increasingly used day in and day out in various applications such as surveillance, security, monitoring, and robotic vision. In this chapter, the authors propose a novel multiple objects tracking system in video sequences that deals with occlusion issues. The proposed system is composed of two components: An improved KLT tracker, and a Kalman filter. The improved KLT tracker uses the basic KLT tracker and an appearance model to track objects from one frame to another and deal with partial occlusion. In partial occlusion, the appearance model (e.g., a RGB color histogram) is used to determine an object’s KLT features, and the authors use these features for accurate and robust tracking. In full occlusion, a Kalman filter is used to predict the object’s new location and connect the trajectory parts. The system is evaluated on different videos and compared with a common tracking system.

[1]  Hironobu Fujiyoshi,et al.  Moving target classification and tracking from real-time video , 1998, Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201).

[2]  Yoshitaka Sakurai,et al.  Adaptive Kansei Search Method Using User's Subjective Criterion Deviation , 2011, Int. J. Comput. Vis. Image Process..

[3]  Dorin Comaniciu,et al.  Kernel-Based Object Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Wei-Yun Yau,et al.  A Bayesian framework for robust human detection and occlusion handling human shape model , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[5]  Ammar Bouallègue,et al.  Chase-Like Decoding of Arithmetic Codes with Applications , 2011, Int. J. Comput. Vis. Image Process..

[6]  Junzo Watada,et al.  VIDEO TRACKING SYSTEM: A SURVEY , 2008 .

[7]  Xin Li,et al.  Contour-based object tracking with occlusion handling in video acquired using mobile cameras , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Djemel Ziou,et al.  Object tracking in videos using adaptive mixture models and active contours , 2008, Neurocomputing.

[9]  Huiyu Zhou,et al.  Object tracking using SIFT features and mean shift , 2009, Comput. Vis. Image Underst..

[10]  A. M. Tekalp,et al.  Multiple camera tracking of interacting and occluded human motion , 2001, Proc. IEEE.

[11]  Rita Cucchiara,et al.  Probabilistic people tracking for occlusion handling , 2004, ICPR 2004.

[12]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Seema Verma,et al.  Data Broadcast Management in Wireless Communication: An Emerging Research Area , 2011 .

[14]  Shaogang Gong,et al.  Tracking colour objects using adaptive mixture models , 1999, Image Vis. Comput..

[15]  In-So Kweon,et al.  Moving object detection and tracking from moving camera , 2011, 2011 8th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI).

[16]  Myron Flickner,et al.  Detection and tracking of shopping groups in stores , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[17]  W. Eric L. Grimson,et al.  Learning Patterns of Activity Using Real-Time Tracking , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[19]  James W. Davis,et al.  The KidsRoom: A Perceptually-Based Interactive and Immersive Story Environment , 1999, Presence.

[20]  Andrew Blake,et al.  A Probabilistic Background Model for Tracking , 2000, ECCV.

[21]  Alex Pentland,et al.  Pfinder: Real-Time Tracking of the Human Body , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Roger J. Green,et al.  Applied Signal and Image Processing: Multidisciplinary Advancements , 2011 .

[23]  P. A. Vijaya,et al.  Machine Vision Based Non-Magnetic Object Detection and Removal on Moving Conveyors in Steel Industry through Differential Techniques , 2012, Int. J. Comput. Vis. Image Process..

[24]  Jiri Matas,et al.  P-N learning: Bootstrapping binary classifiers by structural constraints , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[25]  George D. Giannoglou,et al.  Future Trends in 3D Intravascular Ultrasound (IVUS) Reconstruction , 2012 .

[26]  Harpreet S. Sawhney,et al.  Shapeme histogram projection and matching for partial object recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Sharath Pankanti,et al.  Appearance models for occlusion handling , 2006, Image Vis. Comput..

[28]  Takeo Kanade,et al.  Background Subtraction for Freely Moving Cameras , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[29]  Stephen J. McKenna,et al.  Tracking interacting people , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[30]  Subhash Challa,et al.  Multiple Pedestrian Tracking Using Colour and Motion Models , 2010, 2010 International Conference on Digital Image Computing: Techniques and Applications.

[31]  Rachid Deriche,et al.  Geodesic active regions and level set methods for motion estimation and tracking , 2005, Comput. Vis. Image Underst..

[32]  Yongmin Kim,et al.  Semiautomatic video object segmentation using VSnakes , 2003, IEEE Trans. Circuits Syst. Video Technol..

[33]  Vikas Kumar,et al.  A Novel Approach of Restoration of Digital Images Degraded by Impulse Noise , 2014, Int. J. Comput. Vis. Image Process..

[34]  Larry S. Davis,et al.  Probabilistic framework for segmenting people under occlusion , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[35]  P. KaewTrakulPong,et al.  An Improved Adaptive Background Mixture Model for Real-time Tracking with Shadow Detection , 2002 .

[36]  L. Davis,et al.  Background and foreground modeling using nonparametric kernel density estimation for visual surveillance , 2002, Proc. IEEE.

[37]  D. S. Guru,et al.  2D2LPI: Two Directional Two Dimensional Locality Preserving Indexing , 2013, Int. J. Comput. Vis. Image Process..

[38]  Pheng-Ann Heng,et al.  Two-Stage Object Tracking Method Based on Kernel and Active Contour , 2010, IEEE Transactions on Circuits and Systems for Video Technology.

[39]  Ioannis Brilakis,et al.  Comparative study of vision tracking methods for tracking of construction site resources , 2011 .

[40]  Tao Zhang,et al.  Improving performance of distribution tracking through background mismatch , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[41]  Seong-Whan Lee,et al.  Multiple People Tracking Using a Appearance Model Based on Temporal Color , 2000, Biologically Motivated Computer Vision.

[42]  Andrew Blake,et al.  A Probabilistic Exclusion Principle for Tracking Multiple Objects , 2004, International Journal of Computer Vision.