Robust spatio-temporal context for infrared target tracking

Target tracking is one of the most important and active research areas in the field of computer vision. In this paper, we address the problem of tracking an object that is completely occluded in a video. The proposed robust spatio-temporal context (RSTC) method, inspired by the spatio-temporal context method, uses spatio-temporal context information to establish an early warning mechanism. The core technology of the early warning mechanism is to monitor the fluctuation of the relative change rate between two adjacent frames. When a target is completely occluded, the algorithm estimates the target's location using accurate motion information saved during the early warning. Finally, the algorithm captures the target after the target reappears. Experimental results show that the proposed algorithm is very robust and efficient when used in visual tracking applications.