基于关键帧特征库统计特征的双人交互行为识别

针对双人交互行为识别算法中普遍存在的算法计算复杂度高、识别准确性低的问题，提出一种新的基于关键帧特征库统计特征的双人交互行为识别方法。首先，对预处理后的交互视频分别提取全局GIST和分区域方向梯度直方图（HOG）特征。然后，采用k-means聚类算法对每类动作训练视频的所有帧的特征表示进行聚类，得到若干个近似描述同类动作视频的关键帧特征，构造出训练动作类别对应的关键帧特征库；同时，根据相似性度量统计出特征库中各个关键帧在交互视频中出现的频率，得到一个动作视频的统计直方图特征表示。最后，利用训练后的直方图相交核支持向量机（SVM），对待识别视频采用决策级加权融合的方法得到交互行为的识别结果。在标准数据库测试的结果表明，该方法简单有效，对交互行为的正确识别率达到了85%。

Human interaction recognition based on statistical features of key frame feature library

Some issues such as high computational complexity and low recognition accuracy still exist in human interaction recognition. In order to solve those problems, an innovative and effective method based on statistical features of key frame feature library was proposed. Firstly, features of global GIST and regional Histogram of Oriented Gradient (HOG) were extracted from the pre-processed videos. Secondly, training videos with different kind of actions were clustered by the k-means algorithm respectively to get key frame feature of each action for constructing key frame feature library; in addition, similarity measure was utilized to calculate the frequency of different key frames in every interactive video, then the statistical histogram representation of interactive videos were obtained. Finally, the decision level fusion was achieved by using Support Vector Machine (SVM) classifier based on histogram intersection kernel to obtain impressive results on UT-interaction dataset. The experimental results on standard database show that the correct recognition rate of the proposed method is 85%, which indicates that the proposed method is simple and effective.