Learning a Similarity Metric Discriminatively for Pose Exemplar based Action Recognition

摘要：

Exemplar-based action recognition has the advantages of being compact and time-invariant But how to select suitable exemplars and measure the pose similarities between frames and exemplars are no easy tasks. In this paper, we propose an approach to efficiently select pose exemplars and learn a pose similarity metric between frames and pose exemplars. First, a subset of training frames is mapped into pose space, where clustering is performed to select pose exemplars. Second, a pose similarity metric between frames and pose exemplars is learned based on exemplar classifiers. Finally, both training and testing videos are embedded into a space defined by similarities to pose exemplars, where action classifiers are trained to recognize actions from videos. To test our method, we have used a publicly available dataset which demonstrates that, using very simple features and fewer exemplars, our method can achieve the same or better recognition rate as the state-of-the-art methods.

关键词： human action recognition exemplar embedding pose exemplar similarity metric learning

作者: Taiqing Wang Shengjin Wang Xiaoqing Ding

作者单位: Department of Electronic Engineering Tsinghua University Beijing, P.R. China

会议类型: 国际会议

会议名称: 2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)

会议地点: 上海

会议语种:英文

页码: 414-418

在线出版日期: 2011-10-15（万方平台首次上网日期，不代表论文的发表时间）