Paper Info
Title
Joint tracking with event grouping and temporal constraints
Abstract
Vision systems become more and more popular to be applied in monitoring tasks such as controlling traffic flows or for security issues. The analysis of target behavior is always based on its observed trajectory, which can be acquired by tracking approaches. Although the fashion of tracking-by-detection is favored by the research community, it still faces challenges like unexpected occlusion caused by background objects or other tracked targets, which can interfere the matching operation and result in tracking errors. In this paper, we propose a novel approach by aggregating prediction events within target groups and integrating a graph-modeling based stitching procedure to handle the above mentioned problems. The evaluation results on the UA-DETRAC benchmark demonstrated the state-of-the-art performance of our tracking approach.
Year
DOI
Venue
2017
10.1109/AVSS.2017.8078515
2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
Keywords
Field
DocType
joint tracking,temporal constraints,monitoring tasks,traffic flows,security issues,target behavior,observed trajectory,tracking approach,tracking-by-detection,research community,background objects,tracked targets,matching operation,tracking errors,aggregating prediction events,target groups,graph-modeling based stitching procedure,computer vision systems,stitching procedure
Computer vision,Data mining,Image stitching,MATLAB,Computer science,Tracking system,Artificial intelligence,Benchmark (computing),Trajectory
Conference
ISBN
Citations 
PageRank 
978-1-5386-2940-6
0
0.34
References 
Authors
16
2
Name
Order
Citations
PageRank
Wei Tian12810.31
Martin Lauer2218.98