A Genetic Algorithm-Based Moving Object Detection for Real-time Traffic Surveillance

Recent developments in vision systems such as distributed smart cameras have encouraged researchers to develop advanced computer vision applications suitable to embedded platforms. In the embedded surveillance system, where memory and computing resources are limited, simple and efficient computer vision algorithms are required. In this letter, we present a moving object detection method for real-time traffic surveillance applications. The proposed method is a combination of a genetic dynamic saliency map (GDSM), which is an improved version of dynamic saliency map (DSM) and background subtraction. The experimental results show the effectiveness of the proposed method in detecting moving objects.

[1]  Rachid Deriche,et al.  Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Bernhard Rinner,et al.  A Bright Future for Distributed Smart Cameras , 2008 .

[3]  K. A. Joshi,et al.  A Survey on Moving Object Detection and Tracking in Video Surveillance System , 2012 .

[4]  Minho Lee,et al.  A traffic surveillance system using dynamic saliency map and SVM boosting , 2010 .

[5]  Jin Young Choi,et al.  Intelligent visual surveillance — A survey , 2010 .

[6]  Massimo Piccardi,et al.  Background subtraction techniques: a review , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[7]  Yang Wang,et al.  Real-Time Moving Vehicle Detection With Cast Shadow Removal in Video Based on Conditional Random Field , 2009, IEEE Transactions on Circuits and Systems for Video Technology.

[8]  Dorin Comaniciu,et al.  Real-time tracking of non-rigid objects using mean shift , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[9]  Bernhard Rinner,et al.  An Introduction to Distributed Smart Cameras , 2008, Proceedings of the IEEE.

[10]  Rita Cucchiara,et al.  Improving shadow suppression in moving object detection with HSV color information , 2001, ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585).

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

[12]  Guillaume-Alexandre Bilodeau,et al.  Flexible Background Subtraction with Self-Balanced Local Sensitivity , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[13]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .