Hybrid Algorithm for Segmentation and Tracking in Surveillance

In this paper, an integrated video surveillance system for robust tracking is introduced. In the blob detection part, an optical flow algorithm for crowded environment is studied experimentally and a comparison study with respect to traditional subtraction approach is carried out. In the segmentation part, different algorithms are fused to develop a hybrid algorithm for stable segmentation, and validation rules for successful segmentation are also presented preventing from false segmentation. In the tracking part, a blob's parameter, which we call color spectrum, is developed to identify different persons and track them robustly. A hybrid algorithm for tracking is also developed to combine color tracking with traditional distance tracking. The hybrid algorithms in segmentation and tracking enable the system to track persons when they change movement unpredictably in occlusion. Experimental results validate the proposed algorithm.