[1]CHEN Tingting,RUAN Qiuqi,AN Gaoyun.Slow feature extraction algorithm of human actions in video[J].CAAI Transactions on Intelligent Systems,2015,10(3):381-386.[doi:10.3969/j.issn.1673-4785.201407002]
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Slow feature extraction algorithm of human actions in video

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 3 Page number: 381-386 Column: 学术论文—机器学习 Public date: 2015-06-25
Title:
Slow feature extraction algorithm of human actions in video
Author(s):
CHEN Tingting1; 2;  RUAN Qiuqi1;  AN Gaoyun1
1. Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China;
2. Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing Jiaotong University, Beijing 100044, China
Keywords:
human action; training cuboids; slow feature function; slow feature; frame difference
CLC:
TP391
DOI:
10.3969/j.issn.1673-4785.201407002
Abstract:
Extracting important and distinguishable features from complex human actions is the key for human actions analysis. In recent years, classical feature analysis methods are mostly linear feature analysis technologies, which result in error results for non-linear processing. This paper proposes a method of extracting slow features. First, the image sequence of frame difference was obtained by the difference between the consecutive frames and some feature points of selected beginning frame were detected. Next, the feature points were tracked by optical flow method and the training cuboids were collected. Finally, the slow feature functions were learned with the collected training cuboids, then the slow features could be extracted and represented. In the experiment, slow features of each action were extracted and compared with each other. The results show that the extracted slow features vary slowly with time and action interclass has good discrimination, which suggests that this method can extract slow features from human actions effectively.
References:
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