Human interaction recognition based on sparse representation of feature covariance matrices

Abstract

A new method for interaction recognition based on sparse representation of feature covariance matrices was presented. Firstly, the dense trajectories (DT) extracted from the video were clustered into different groups to eliminate the irrelevant trajectories, which could greatly reduce the noise influence on feature extraction. Then, the trajectory tunnels were characterized by means of feature covariance matrices. In this way, the discriminative descriptors could be extracted, which was also an effective solution to the problem that the description of the feature second-order statistics is insufficient. After that, an over-complete dictionary was learned with the descriptors and all the descriptors were encoded using sparse coding (SC). Classification was achieved using multiple instance learning (MIL), which was more suitable for complex environments. The proposed method was tested and evaluated on the WEB Interaction dataset and the UT interaction dataset. The experimental results demonstrated the superior efficiency.

摘要

人体行为识别是计算机视觉和模式识别领域的一个重要研究方向, 在监控系统、 人机交互、 人工智能等方面具有广阔的应用前景。 本文提出了一种基于协方差矩阵稀疏表示的交互行为识别方法。 首先, 对视频中提取的稠密轨迹进行聚类形成不同的轨迹群组, 以消除无关轨迹、 减少噪声对特征提取的影响。 然后通过协方差矩阵对轨迹通道进行特征描述, 得到有较强区分度的轨迹通道描述符, 该描述符维度更低, 并且能够有效解决以往描述符对特征二阶统计量描述不足的问题; 利用稀疏表示对特征描述符进行稀疏编码。 最后, 采用多示例学习进行行为分类。 在 UT-Interaction 数据集与 WEB-Interaction 数据集上的实验证明了本文方法的有效性。