An improved target tracking algorithm and its application in intelligent video surveillance system

Target tracking is one of the pivotal technologies in intelligent video surveillance systems. Facing the complex and various scenarios in practical applications, improving the accuracy and real-time of target detection and tracking is has become the goal of current monitoring systems. Firstly, the target feature expression model is established by fusing Sobel Median Binary Pattern (SMBP) and H-S features while the final target probability model is set up by a weighted color kernel function histogram. Secondly, the final target probability model is established by fusing a weighted color kernel function histogram. Thirdly, the improved unscented Kalman particle filtering algorithm proposed in this paper is embedded in the target tracking framework to complete the target tracking. Lastly, compared with the traditional tracking algorithm, the experiments results show that the target tracking algorithm proposed in this paper improves the tracking accuracy by about 4%.