Abstract
To solve the problems of tracking errors such as target missing that emerged in compressive tracking (CT) algorithm due to factors such as pose variation, illumination change, and occlusion, a novel tracking algorithm combined angular point matching with compressive tracking (APMCCT) was proposed. A sparse measurement matrix was adopted to extract the Haar-like features. The offset of the predicted target position was integrated into the angular point matching, and the new target position was calculated. Furthermore, the updating mechanism of the template was optimized. Experiments on different video sequences have shown that the proposed APMCCT performs better than CT algorithm in terms of accuracy and robustness and adaptability to pose variation, illumination change, and occlusion.
Highlights
Target tracking is very important in the field of computer vision, involved in intelligent transportation, monitoring security, vision navigation, and other civil and military fields
Aiming at the problem in literature [13], this paper proposed an algorithm combined angular point matching with compressive tracking (APMCCT)
In order to verify the robustness of the new algorithm, the performance of APMCCT algorithm was compared with the original compression tracking
Summary
Target tracking is very important in the field of computer vision, involved in intelligent transportation, monitoring security, vision navigation, and other civil and military fields. To study moving target tracking, it would be classified into two categories: (a) directly detect and identify the target in image sequences independent on the prior knowledge, and find the location of interested target; (b) first of all, build the model according to the prior knowledge, and find the target accurately in real time in the subsequent frames [4] Based on these two ideas, a variety of effective target tracking algorithms were derived. In literature [12], Mei and Ling proposed a robust L1 tracking algorithm in the particle filter framework They regarded the target tracking problem as a sparse approximation problem and regarded the candidate which has minimum projection error as tracking target through a l1 norm least squares solution. To make the APMCCT algorithm much stronger and robust, the updating mechanism of the template was optimized
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
