Segmentation-tracking feedback approach for high-performance video surveillance applications

Here, a novel and efficient feedback system for moving object segmentation and tracking is proposed. Through the use of non-parametric background-foreground modeling, moving objects are correctly detected in unfavorable situations such as dynamic backgrounds or illumination changes. After detection, objects are tracked by an original multi-object Bayesian tracking algorithm, which achieves satisfactory results under partial and total occlusions. Updating the previously detected foreground data from the information provided by the tracker, the foreground modeling is improved, reducing the color similarity problem.