Abstract
Multi-camera vehicle tracking at the city scale has received lots of attention in the last few years. It has large-scale differences, frequent occlusion, and appearance differences caused by the viewing angle differences, which is quite challenging. In this research, we propose the Tracklet Similarity Network (TSN) for a multi-target multi-camera (MTMC) vehicle tracking system based on the evaluation of the similarity between vehicle tracklets. In addition, a novel component, Candidates Intersection Ratio (CIR), is proposed to refine the similarity. It provides an associate scheme to build the multi-camera tracking results as a tree structure. Based on these components, an end-to-end vehicle tracking system is proposed. The experimental results demonstrate that an 11% improvement on the evaluation score is obtained compared to the conventional similarity baseline.
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.
