By a News Reporter-Staff News Editor at Information Technology Newsweekly -- Investigators publish new report on Information Technology. According to news reporting from Beijing, People's Republic of China, by VerticalNews journalists, research stated, "Soccer player tracking and labeling suffer from the similar appearance of the players in the same team, especially in long-shot scenes where the faces and the numbers of the players are too blurry to identify. In this paper, we propose an efficient multi-player tracking system."
The news correspondents obtained a quote from the research from the Chinese Academy of Science, "The tracking system takes the detection responses of a human detector as inputs. To realize real-time player detection, we generate a spatial proposal to minimize the scanning scope of the detector. The tracking system utilizes the discriminative appearance models trained using the online Boosting method to reduce data-association ambiguity caused by the appearance similarity of the players. We also propose to build an online learned player recognition model which can be embedded in the tracking system to approach online player recognition and labeling in tracking applications for long-shot scenes by two stages. At the first stage, to build the model, we utilize the fast k-means clustering method instead of classic k-means clustering to build and update a visual word vocabulary in an efficient online manner, using the informative descriptors extracted from the training samples drawn at each time step of multi-player tracking. The first stage finishes when the vocabulary is ready. At the second stage, given the obtained visual word vocabulary, an incremental vector quantization strategy is used to recognize and label each tracked player. We also perform importance recognition validation to avoid mistakenly recognizing an outlier, namely, people we do not need to recognize, as a player."
According to the news reporters, the research concluded: "Both quantitative and qualitative experimental results on the long-shot video clips of a real soccer game video demonstrate that, the proposed player recognition model performs much better than some state-of-the-art online learned models, and our tracking system also performs quite effectively even under very complicated situations."
For more information on this research see: Online Learned Player Recognition Model Based Soccer Player Tracking and Labeling for Long-Shot Scenes. IEICE Transactions on Information and Systems, 2014;E97D(1):119-129. IEICE Transactions on Information and Systems can be contacted at: Ieice-Inst Electronics Information Communications Eng, Kikai-Shinko-Kaikan Bldg, 3-5-8, Shiba-Koen, Minato-Ku, Tokyo, 105-0011, Japan.
Our news journalists report that additional information may be obtained by contacting W.C. Xu, Chinese Academy Sci, Inst Informat Engn, Beijing, People's Republic of China. Additional authors for this research include Q.J. Zhao, Y.X. Wang and X.Y. Li.
Keywords for this news article include: Asia, Beijing, Information Technology, People's Republic of China
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