A Fast Object Detecting-Tracking Method in Compressed Domain

The traditional pixel domain tracking algorithms are often applied to rigid objects which move slowly in simple background. But it performs very poor for non-rigid object tracking. In order to solve this problem, this paper proposes a tracking method of rapid detection in compressed domain. Convex hull formed by Self-adaptive boundary searching method and rule-based clustering are adopted for the detector in order to reduce the complexity of the algorithm. At the tracking stage, Kalman filtering is used to forecast the location of the objective. Meanwhile, as the whole process is completed in the compressed domain, it can meet the real-time requirement compared with other algorithms. And it tracks the target more precisely. The experimental results show that the proposed method has the following properties: (1) more advantages in tracking small-sized objects; (2) a better effect when track a fast moving objects; (3) faster tracking speed.