Title
Robust multiple cue fusion-based high-speed and nonrigid object tracking algorithm for short track speed skating.
Abstract
This paper presents a methodology for tracking multiple skaters in short track speed skating competitions. Nonrigid skaters move at high speed with severe occlusions happening frequently among them. The camera is panned quickly in order to capture the skaters in a large and dynamic scene. To automatically track the skaters and precisely output their trajectories becomes a challenging task in object tracking. We employ the global rink information to compensate camera motion and obtain the global spatial information of skaters, utilize random forest to fuse multiple cues and predict the blob of each skater, and finally apply a silhouette- and edge-based template-matching and blob-evolving method to labelling pixels to a skater. The effectiveness and robustness of the proposed method are verified through thorough experiments. (C) 2016 SPIE and IS&T
Year
DOI
Venue
2016
10.1117/1.JEI.25.1.013014
JOURNAL OF ELECTRONIC IMAGING
Keywords
Field
DocType
multiple object tracking,multiple cue fusion,random forest,dynamic appearance model
Spatial analysis,Computer vision,Pattern recognition,Silhouette,Computer science,Robustness (computer science),Video tracking,Blob detection,Artificial intelligence,Pixel,Fuse (electrical),Random forest
Journal
Volume
Issue
ISSN
25
1
1017-9909
Citations 
PageRank 
References 
1
0.36
0
Authors
5
Name
Order
Citations
PageRank
Chenguang Liu110.69
H. D. Cheng21900138.13
Yingtao Zhang3122.36
Yuxuan Wang414412.04
Min Xian5215.84