关键词: 目标跟踪, 时空上下文, 检测分类器, 目标时空模型, 置信图

Abstract:

In the single-target long-term tracking process, in order to avoid the effects of rapid movement and motion blur noise, and deal with the problem of tracking failure recovery mechanism, this paper presents a TLD (tracking-learning-detection) object tracking algorithm based on spatio-temporal context similarity (TLD-STCS). Firstly, conduct a detector study. Then, use the STC tracking algorithm the next frame, and calculate the approximation of the candidate target and the previous frame, including conservative similarity and motion similarity, to track the results of the effective judgments, if the decision is valid, the output process is the same as that of the TLD, if the judgment fails, the temporal space model at this time is added to the target space-time model. Next, calculate the confidence pattern for the multiple candidate target position detected by the detection module, output the detection target with the highest average confidence value, and update the target space-time model, a single cluster is detected for direct output. Finally, update the relevant parameters of classifier to improve the detection accuracy by online learning. Conducting experiments in the benchmark data set demonstrate the success of TLD-STCS. The results show that TLD-STCS algorithm can adapt to target tracking, rotation and other complex scenarios under the target tracking, has a high degree of robustness, especially in the case of fast moving and moving blur with good anti-interference ability and high success rate.

Key words: target tracking, spatio-temporal context, detection classifier, target space-time model, confidence map