Spatio-Temporal Context-based Object Tracking Algorithm in Video Surveillance

The entire video surveillance industry has rapidly entered the era of intelligent surveillance. However, the environment is complex and changeable in the video obtained under natural uncontrolled conditions, which poses many challenges for the detection and tracking of various types of pedestrian targets. For a variety of complex scenarios and different goals, how to design a target tracking and recognition technology with high efficiency, good robustness, and strong real-time performance is still a hot and difficult topic in the industry. Therefore, this paper focuses on non-rigid pedestrians in the monitoring field, and uses multi-features collaborative learning to analyze and research object tracking. It is intended to achieve Spatio-Temporal Context model for tracking, especially to improve the accuracy of the descendant tracking in a complex background. Experiments show that the overlapping rate (OR) and tracking center location error (CLE) for our proposed algorithm exceed the existing optimal algorithms, and its tracking speed is more than that of most algorithms.