A correlative classifiers approach based on particle filter and sample set for tracking occluded target

• Published in: Applied Mathematics-A Journal of Chinese Universities Vol. 32; no. 3; pp. 294 - 312
• Main Authors: Li, Kang, He, Fa-zhi, Yu, Hai-ping, Chen, Xiao
• Format: Journal Article
• Language: English
• Published: Hangzhou Editorial Committee of Applied Mathematics - A Journal of Chinese Universities 01.09.2017; Springer Nature B.V
• Subjects: Applications of Mathematics; Classifiers; Computer vision; Correlation; Feature extraction; Mathematics; Mathematics and Statistics; Occlusion; Samples; Similarity; Statistical analysis; Statistical methods; Tracking; 65L15; visual tracking; 35B35; 60G40; sample set method; particle filter; online learning
• ISSN: 1005-1031; 1993-0445
• DOI: 10.1007/s11766-017-3466-8

Target tracking is one of the most important issues in computer vision and has been applied in many fields of science, engineering and industry. Because of the occlusion during tracking, typical approaches with single classifier learn much of occluding background information which results in the decrease of tracking performance, and eventually lead to the failure of the tracking algorithm. This paper presents a new correlative classifiers approach to address the above problem. Our idea is to derive a group of correlative classifiers based on sample set method. Then we propose strategy to establish the classifiers and to query the suitable classifiers for the next frame tracking. In order to deal with nonlinear problem, particle filter is adopted and integrated with sample set method. For choosing the target from candidate particles, we define a similarity measurement between particles and sample set. The proposed sample set method includes the following steps. First, we cropped positive samples set around the target and negative samples set far away from the target. Second, we extracted average Haar-like feature from these samples and calculate their statistical characteristic which represents the target model. Third, we define the similarity measurement based on the statistical characteristic of these two sets to judge the similarity between candidate particles and target model. Finally, we choose the largest similarity score particle as the target in the new frame. A number of experiments show the robustness and efficiency of the proposed approach when compared with other state-of-the-art trackers.