Automatic multiple human detection and tracking for visual surveillance system

Object Tracking is an important task in video processing because of its variety of applications in visual surveillance, human activity monitoring and recognition, traffic flow management etc. Multiple object detection and tracking in outdoor environment is a challenging task because of the problems raised by poor lighting conditions, variation in poses of human object, shape, size, clothing, etc. This paper proposes a novel technique for detection and tracking of multiple human objects in a video. A classifier is trained for object detection using Haar-like features from training image set. Human objects are detected with help of this trained detector and are tracked using particle filter. The experimental results show that the proposed technique can detect and track multiple humans in a video adequately fast in the presence of poor lighting conditions, variation in poses of human objects, shape, size, clothing etc. and the technique can handle varying number of human objects in a video at various points of time.

[1]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[2]  Ashish Khare,et al.  Object Tracking of Video Sequences in Curvelet Domain , 2011, Int. J. Image Graph..

[3]  D. Koller,et al.  Towards robust automatic traffic scene analysis in real-time , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[4]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[5]  Thierry Chateau,et al.  Illumination aware MCMC Particle Filter for long-term outdoor multi-object simultaneous tracking and classification , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[6]  Michael Isard,et al.  A mixed-state condensation tracker with automatic model-switching , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[7]  Hui Wang,et al.  An Efficient Multi-object Tracking Method Using Multiple Particle Filters , 2009, 2009 WRI World Congress on Computer Science and Information Engineering.

[8]  Larry S. Davis,et al.  W/sup 4/: Who? When? Where? What? A real time system for detecting and tracking people , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[9]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[10]  Yuan Chen,et al.  An Improved Color-Based Particle Filter for Object Tracking , 2008, 2008 Second International Conference on Genetic and Evolutionary Computing.

[11]  Cedric Nishan Canagarajah,et al.  Particle filtering with multiple cues for object tracking in video sequences , 2005, IS&T/SPIE Electronic Imaging.

[12]  Xiongcai Cai,et al.  Robust object tracking using the particle filtering and level set methods: A comparative experiment , 2008, 2008 IEEE 10th Workshop on Multimedia Signal Processing.

[13]  Michael Isard,et al.  BraMBLe: a Bayesian multiple-blob tracker , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[14]  Branko Ristic,et al.  A color-based particle filter for joint detection and tracking of multiple objects , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[15]  Ashish Khare,et al.  Curvelet transform-based technique for tracking of moving objects , 2012 .

[16]  Jean-Charles Noyer,et al.  Object detection and tracking using the particle filtering , 2003, The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003.

[17]  PeopleIsmail,et al.  W 4 : Who ? When ? Where ? What ? A Real Time System for Detecting and Tracking , 1998 .

[18]  Tieniu Tan,et al.  A survey on visual surveillance of object motion and behaviors , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[19]  Carlo S. Regazzoni,et al.  Real-time video-shot detection for scene surveillance applications , 2000, IEEE Trans. Image Process..