Human Activity Recognition using Optical Flow based Feature Set

被引：0

作者：

Kumar, S. Santhosh ^{[1
]}

John, Mala ^{[1
]}

机构：

[1] Anna Univ, Dept Elect Engn, Madras Inst Technol, Madras, Tamil Nadu, India

来源：

2016 IEEE INTERNATIONAL CARNAHAN CONFERENCE ON SECURITY TECHNOLOGY (ICCST) | 2016年

关键词：

optical flow; feature descriptor; support vector machine; classification; human activity recognition; VIDEOS;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

An optical flow based approach for recognizing human actions and human-human interactions in video sequences has been addressed in this paper. We propose a local descriptor built by optical flow vectors along the edges of the action performer(s). By using the proposed feature descriptor with multi-class SVM classifier, recognition rates as high as 95.69% and 94.62% have been achieved for Weizmann action dataset and KTH action dataset respectively. The recognition rate achieved is 92.7% for UT interaction Set_1, 90.21% for UT interaction Set_2. The results demonstrate that the method is simple and efficient.

引用

页码：138 / 142

页数：5

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