基于尺度自适应局部时空特征的足球比赛视频中的多运动员行为表示  

Behavior representation of multi-athletes for football game video based on scale adaptive local spatial and temporal characteristics

作　　者：王智文[1] 蒋联源[1,2] 王宇航[3] 王日凤[1] 张灿龙[4] 黄镇谨[1,2] 王鹏涛[5] 

机构地区：[1]广西科技大学计算机科学与通信工程学院,广西柳州545006 [2]桂林电子科技大学广西信息科学实验中心,广西桂林541004 [3]桂林航天工业学院汽车与交通工程学院,广西桂林541004 [4]广西师范大学计算机科学与信息工程学院,广西桂林541004 [5]广西科技大学电气与信息工程学院,广西柳州545006

出　　处：《计算机应用》2016年第8期2134-2138,共5页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61462008;61440017;61365009);广西自然科学基金项目(2013GXNSFAA019336;2014GXNSFAA118368);广西信息科学实验中心开放基金项目(KF1403);广西科技大学博士基金项目(院科博12Z14);2015年广西科技大学创新团队项目~~

摘　　要：为提高足球比赛视频中的多运动员行为识别的准确率,提出一种基于尺度自适应局部时空特征的足球比赛视频中的多运动员行为表示方法,利用时空兴趣点来表示足球比赛视频中的多运动员行为。首先将足球比赛视频序列中的多运动员行为看作是三维空间中的时空兴趣点的集合,然后采用直方图量化技术将时空兴趣点集合量化为维数固定的直方图(即时空单词),最后采用K-means聚类算法生成时空码本。在聚类生成码本之前,对每个时空兴趣点都进行了归一化,以保证其缩放和平移不变性。实验结果表明,该方法能够大大减少足球比赛视频中的多运动员行为识别算法的计算量,显著提高识别的准确率。In order to improve the accuracy of behavior recognition of multi-athletes in football game video, a behavior representation method of multi-athletes for video football game based on scale adaptive local spatial and temporal characteristics was put forward. Behavior recognition was carried on using spatial-timporal interest point to represent behavior of multi-athletes in video football game. Firstly, multi-athletes behavior in the sequence of video football game was regarded as a collection of spatial-timporal interest points in three-dimensional space. Secondly, the set of spatial-temporal interest points was quantified as histogram which has fixed dimension （ ie temporal word） by using quantitative technique of histogram. Finally, spatial- temporal codebook was generated by using K-means clustering algorithm. Each spatial-timporal interest point was normalized to ensure its scaling and translation invariance before clustering codebook generated. Experimental results show that the proposed method can greatly reduce the computational amount of the algorithm, and the accuracy of recognition can be significantly improved.

关 键 词：时空兴趣点 多运动员行为表示 行为识别 K-MEANS聚类算法 时空特征检测操作数 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论] TP391[自动化与计算机技术—计算机科学与技术]