基于改进的特征图串法识别人体行为

视频的有效表达是识别行为关键与难点。本文提出了一种改进的特征图串的视频表达方法，在动态规划框架下，利用子模优化方法和图匹配技术实现了行为的识别。首先，利用近年来被广泛应用的时空特征点探测器获取视频序列中的关键点；接着引入子模优化方法完成视频在时域上的划分；然后在每个时域区间内以关键点为节点形成图结构，使得行为视频的特征表示转化为有序的特征图串；最后基于重加权随机游走的图匹配方法和动态时间规整实现成对视频的匹配与对齐。通过两组公开数据集（KTH和UT-interaction）上的实验及与其他方法的比较，验证了本文方法是有效的、可行的。

Recognizing Human Action based on the improved String of Feature Graphs method

the effective representation of the video is the key and difficulty in human action recognition. In this paper, we proposed an improved string of feature graphs method to describe an video, which combines submodular optimization method and graphic matching technique in the framework of dynamic programming. Firstly, space-time feature points in a video are obtained by utilizing spatio-time interest point detector, and leveraging the submodular means considering the time order divides the video into many small time intervals. Then the representation of the video can be transformed into a string graphs which are constructed based on these feature points falling in the corresponding time interval. Finally, measuring the similarity of pair of videos is implemented through using the techniques of the Reweighted Random Walks for Graph Matching (RRWM) and Dynamic Time Warping (DTW) between string graphs from two videos respectively. Here we provide comparisons against other methods on the two published datasets (KTH and UT-interaction) and the results demonstrate that this algorithm is effective and feasible.