运动员的动作行为分析是体育视频高级语义分析的直接途径,检测和分割视频中的运动员是分析运动员的动作行为的基础.利用体育视频的领域规则和中层特征块的性质,提出半监督的方法挖掘运动员的中层特征块,针对不同类型的镜头分别训练基于中层特征块的运动员检测分类器,实现运动员检测.利用运动员检测结果标记超像素,结合Grab Cut分割算法实现运动员分割.实验结果表明,基于中层特征块的运动员区域检测算法能够快速挖掘训练所需样本,从而训练得到检测分类器,检测结果具有较高的准确度,获得的运动员区域能够有效用于运动员分割,简化了分割计算过程.

Action and behavior analysis of players is a direct method of high-level semantic analysis or highlight annotation in sports video. Accurate detection and segmentation of players is the key technology of this method. Employing domain knowledge and characteristics of mid-level feature patch in sports video, a semi-supervised algorithm is proposed to discover the mid-level feature patch and train the player detector for different types of video shots. The detection result is used to label the superpixel, and then player segmentation is accomplished by Grab Cut segmentation algorithm. Experimental results show that the mid-level feature patch based player detector is convenient to train and achieves high detection accuracy. The detected player regions can be used to segment the players effectively, and hence the computation procedure of player segmentation is simplified.

国家自然科学基金(61173114);武汉市应用基础研究计划(2014010101010027)