关键词: 目标跟踪, 相关滤波, 置信图, 自适应, 重新检测

Abstract:

Correlation filter-based tracking algorithms have shown excellent performance in the field of computer vision, but traditional correlation filters are prone to model drift in complex environments due to the use of fixed coefficient update strategies, and even fail to retrieve the tracked targets and lead to tracking failed. In order to make the tracking algorithm has better robustness when encountering background clutter and occlusion, this paper proposes an association tracking algorithm based on adaptive update strategy and redetection technology. The adaptive update strategy adaptively adjusts the template update coefficient according to the confidence of the tracking result to reduce the impact of the model drift. When it is determined that the tracked target is severely occluded or the tracking fails, the SVM classifier in the redetection strategy is used to redetect the tracked target to improve the error correction capability. The proposed algorithm is verified on the OTB2013 standard target tracking dataset and compared with the other five tracking algorithms. The target tracking accuracy and success rate are increased by 13.8% and 17.4%, respectively. When the target is occluded or the target field of view is lost, the algorithm can still retrieve the target and achieve stable tracking.

Key words: object tracking, correlation filter, confidence map, adaptive, recheck