计算机工程与科学2019，Vol.41Issue(2)：293-301,9.DOI:10.3969/j.issn.1007-130X.2019.02.015
基于KCF相似度的TLD目标跟踪算法
A TLD object tracking algorithm based on KCF similarity
摘要
Abstract
The architecture of the tracking-learning-detection (TLD) algorithm is a good reference for studying long-time single object tracking algorithms. However, due to some defects of its own,the TLD algorithm tends to cause the accumulation of errors and the occurrence of losing objects in complex situations such as fast moving, occlusion and light changes. Given the limitation of the median flow algorithm as the tracker in the tracking module of the TLD algorithm, we propose a TLD object tracking algorithm based on KCF similarity (TLD-KCFS). The KCF algorithm is used to monitor the TLD tracking in real time. The similarity is calculated via the tracking results to judge the switching of the detection module, and the bounding box is adjusted by the combination of the two results. Tests on several different types of videos show that the TLD-KCFS algorithm can achieve stable and good tracking output in complex situations such as blur, fast moving, occlusion, and light changes. It is robust and suitable for long-time object tracking.关键词
跟踪枢相似度/可信跟踪点/跟踪框调整Key words
bounding box similarity/trusted tracking point/bounding box adjustment分类
信息技术与安全科学引用本文复制引用
张晶,熊晓雨,鲍益波..基于KCF相似度的TLD目标跟踪算法[J].计算机工程与科学,2019,41(2):293-301,9.基金项目
国家自然科学基金(61562051) （61562051）
