NDLI: Automatic player labeling, tracking and field registration and trajectory mapping in broadcast soccer video

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Automatic player labeling, tracking and field registration and trajectory mapping in broadcast soccer video

Content Provider	ACM Digital Library
Author	Zhang, Yimin Tong, Xiaofeng Wang, Tao Liu, Jia
Copyright Year	2011
Abstract	In this article, we present a method to perform automatic player trajectories mapping based on player detection, unsupervised labeling, efficient multi-object tracking, and playfield registration in broadcast soccer videos. Player detector determines the players' positions and scales by combining the ability of dominant color based background subtraction and a boosting detector with Haar features. We first learn the dominant color with accumulate color histogram at the beginning of processing, then use the player detector to collect hundreds of player samples, and learn player appearance codebook by unsupervised clustering. In a soccer game, a player can be labeled as one of four categories: two teams, referee or outlier. The learning capability enables the method to be generalized well to different videos without any manual initialization. With the dominant color and player appearance model, we can locate and label each player. After that, we perform multi-object tracking by using Markov Chain Monte Carlo (MCMC) data association to generate player trajectories. Some data driven dynamics are proposed to improve the Markov chain's efficiency, such as label consistency, motion consistency, and track length, etc. Finally, we extract key-points and find the mapping from an image plane to the standard field model, and then map players' position and trajectories to the field. A large quantity of experimental results on FIFA World Cup 2006 videos demonstrate that this method can reach high detection and labeling precision, reliably tracking in scenes of player occlusion, moderate camera motion and pose variation, and yield promising field registration results.
Starting Page	1
Ending Page	32
Page Count	32
File Format	PDF
ISSN	21576904
e-ISSN	21576912
DOI	10.1145/1899412.1899419
Volume Number	2
Issue Number	2
Journal	ACM Transactions on Intelligent Systems and Technology (TIST)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-02-24
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Boosting MCMC Codebook Field registration Multiple player tracking Player labeling Sports video Video analysis
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Theoretical Computer Science

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