Fast and robust algorithm of tracking multiple moving objects for intelligent video surveillance systems

This paper deals with an intelligent image processing method for the video surveillance systems. We propose a technology detecting and tracking multiple moving objects, which can be applied to consumer electronics such as home and business surveillance systems consisting of an internet protocol (IP) camera and a network video recorder (NVR). A real-time surveillance system needs to detect moving objects robustly against noises and environment. So the proposed method uses the red-green-blue (RGB) color background modeling with a sensitivity parameter to extract moving regions, the morphology to eliminate noises, and the blob-labeling to group moving objects. To track moving objects fast, the proposed method predicts the velocity and the direction of the groups formed by moving objects. Finally, the experiments show that the proposed method has the robustness against the environmental influences and the speed, which are suitable for the real- time surveillance system.

[1]  Peter H. N. de With,et al.  Automatic video-based human motion analyzer for consumer surveillance system , 2009, IEEE Transactions on Consumer Electronics.

[2]  Xinhua Zhuang,et al.  Image Analysis Using Mathematical Morphology , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Rashid Ansari,et al.  Multiple object tracking with kernel particle filter , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

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

[5]  A. Hampapur,et al.  Smart video surveillance: exploring the concept of multiscale spatiotemporal tracking , 2005, IEEE Signal Processing Magazine.

[6]  Anthony Vetro,et al.  IEEE TRANSACTIONS ON CONSUMER ELECTRONICS , 2008 .

[7]  Mubarak Shah,et al.  Tracking and Object Classification for Automated Surveillance , 2002, ECCV.

[8]  Nasser Kehtarnavaz,et al.  An Object-Tracking Algorithm Based on Multiple-Model Particle Filtering With State Partitioning , 2009, IEEE Transactions on Instrumentation and Measurement.

[9]  Mongi A. Abidi,et al.  Real-time video tracking using PTZ cameras , 2003, International Conference on Quality Control by Artificial Vision.

[10]  M. Haseyama,et al.  Two-Phased Region Integration Approach for Effective Pedestrian Detection in Low Contrast Images , 2008, 2008 Digest of Technical Papers - International Conference on Consumer Electronics.

[11]  Tim J. Ellis,et al.  Automatic learning of an activity-based semantic scene model , 2003, Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance, 2003..

[12]  Chun-Jen Chen,et al.  A linear-time component-labeling algorithm using contour tracing technique , 2004, Comput. Vis. Image Underst..

[13]  Mansour Moniri,et al.  Classification of smart video surveillance systems for commercial applications , 2005, IEEE Conference on Advanced Video and Signal Based Surveillance, 2005..

[14]  Sharath Pankanti,et al.  Smart Video Surveillance , 2005 .

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

[16]  Sergio A. Velastin,et al.  A review of the state-of-the-art in distributed surveillance systems , 2006 .

[17]  Rui Zhang,et al.  Moving Objects Detection Method Based on Brightness Distortion and Chromaticity Distortion , 2007, IEEE Transactions on Consumer Electronics.