Title
A novel dynamic model for multiple pedestrians tracking in extremely crowded scenarios
Abstract
Tracking hundreds of persons in the large and high density scenarios is a particularly challenging task due to the frequent occlusions and merged measurements. In such circumstances, a stronger dynamic model for prediction usually plays a more important role in the overall tracking process. In this paper, we propose an elaborate dynamic model for multiple pedestrians tracking in the extremely crowded environments. The novelty of this tracking model is that: the global semantic scene structure, local instantaneous crowd flow and the social interactions among persons are taken into account together and combined into an unified approach, which can make the prediction for persons' motion more powerful and accurate. We apply the proposed model by using an online ''tracking-learning'' framework, which can not only perform the robust tracking in the extremely crowded scenarios, but also ensures that the entire process is fully automatic and online. The testing is conducted on the JR subway station of Tokyo, and the experimental results show that the system with our tracking model can robustly track more than 180 targets at the same time while the occlusions and merge/split frequently occur.
Year
DOI
Venue
2013
10.1016/j.inffus.2012.08.004
Information Fusion
Keywords
Field
DocType
elaborate dynamic model,multiple pedestrian,overall tracking process,jr subway station,crowded scenario,crowded environment,stronger dynamic model,entire process,robust tracking,tracking model,novel dynamic model
Computer vision,Multi target tracking,High density,Artificial intelligence,Novelty,Merge (version control),Mathematics
Journal
Volume
Issue
ISSN
14
3
1566-2535
Citations 
PageRank 
References 
3
0.40
28
Authors
6
Name
Order
Citations
PageRank
Xuan Song139236.15
Xiaowei Shao223925.25
Quanshi Zhang328826.67
Ryosuke Shibasaki4955111.58
Huijing Zhao5104677.52
Hongbin Zha62206183.36