Robust event detection scheme for complex scenes in video surveillance

Event detection for video surveillance is a difficult task due to many challenges: cluttered background, illumination variations, scale variations, occlusions among people, etc. We propose an effective and efficient event detection scheme in such complex situations. Moving shadows due to illumination are tackled with a segmentation method with shadow detection, and scale variations are taken care of using the CamShift guided particle filter tracking algorithm. For event modeling, hidden Markov models are employed. The proposed scheme also reduces the overall computational cost by combing two human detection algorithms and using tracking information to aid human detection. Experimental results on TRECVid event detection evaluation demonstrate the efficacy of the proposed scheme. It is robust, especially to moving shadows and scale variations. Employing the scheme, we achieved the best run results for two events in the TRECVid benchmarking evaluation.

[1]  Juan Carlos Niebles,et al.  Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words , 2008, International Journal of Computer Vision.

[2]  Christian Micheloni,et al.  Intelligent Monitoring of Complex Environments , 2010, IEEE Intelligent Systems.

[3]  Krystian Mikolajczyk,et al.  Action recognition with motion-appearance vocabulary forest , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Xiaokang Yang,et al.  Camshift Guided Particle Filter for Visual Tracking , 2007, 2007 IEEE Workshop on Signal Processing Systems.

[5]  Paul Over,et al.  Evaluation campaigns and TRECVid , 2006, MIR '06.

[6]  Nuno Vasconcelos,et al.  Anomaly detection in crowded scenes , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  Gian Luca Foresti,et al.  Trajectory-Based Anomalous Event Detection , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[8]  Zhou Yu,et al.  Shanghai Jiao Tong University participation in high-level feature extraction and surveillance event detection at TRECVID 2009 , 2009 .

[9]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[10]  Chih-Wen Su,et al.  Real-time event detection and its application to surveillance systems , 2006, 2006 IEEE International Symposium on Circuits and Systems.

[11]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[12]  Mohan M. Trivedi,et al.  Detecting Moving Shadows: Algorithms and Evaluation , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Ramakant Nevatia,et al.  Tracking multiple humans in complex situations , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[16]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[17]  Xiaokang Yang,et al.  Shanghai Jiao Tong University participation in high-level feature extraction, automatic search and surveillance event detectionat TRECVID 2008 , 2008, TRECVID.

[18]  Jean Ponce,et al.  Automatic annotation of human actions in video , 2009, 2009 IEEE 12th International Conference on Computer Vision.