Real-Time Target Detection Algorithm Based on Background Modeling

Identifying moving objects from sequence video is a fundamental and critical task in intelligent surveillance applications. In this paper, we design an intelligent video surveillance terminal based on DSP+FPGA structure. Considering the hardware resources, the real-time target detection algorithm is the most important factor. In fact, the common approach which identified moving objects from the portion of a video frame for static camera is background modeling algorithm. However, this paper introduces and simulates five background modeling algorithms, analyzes the simulation results, tests the real-time nature of each algorithm, and selects the best algorithm transplant to hardware platform to achieve a real-time intelligent video surveillance.

[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]  Rita Cucchiara,et al.  Detecting Moving Objects, Ghosts, and Shadows in Video Streams , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

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

[4]  Alex Pentland,et al.  Pfinder: real-time tracking of the human body , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

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

[6]  Larry S. Davis,et al.  Real-time foreground-background segmentation using codebook model , 2005, Real Time Imaging.

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

[8]  W. Eric L. Grimson,et al.  Using adaptive tracking to classify and monitor activities in a site , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[9]  Larry S. Davis,et al.  W4: Real-Time Surveillance of People and Their Activities , 2000, IEEE Trans. Pattern Anal. Mach. Intell..