Paper Info
Title
Improving Object Visual Tracking Performance By Scene Occluder Estimation For Video Surveillance
Abstract
In this paper, we propose an approach for improving the object tracking accuracy in video surveillance scenarios by estimating and compensating the occlusion introduced by static scene objects. Specifically, the scene occluder map is first estimated by analyzing the gradient of a normalized cumulative motion map from the frames of the first several minutes of a surveillance video. Then, a scene occlusion compensation approach for Mean Shift tracking is proposed to improve the object tracking accuracy by using the estimated scene occluder map. Experimental results on two public data sets demonstrate the effectiveness of the proposed approach.
Year
DOI
Venue
2016
10.1109/ICInfA.2016.7832117
2016 IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION (ICIA)
Keywords
Field
DocType
Object tracking, scene occluder estimation, scene occlusion compensation, Mean Shift tracking, video surveillance
Computer vision,Data set,Normalization (statistics),Computer graphics (images),Computer science,Visualization,Tracking system,Video tracking,Eye tracking,Artificial intelligence,Mean-shift
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Lu Wang142.10
Lisheng Xu217839.09
Liling Hao371.49
Qingxu Deng400.34
Max Q.-H. Meng51477202.72