Improved balanced binary tree based action recognition

Action recognition is one of the core content of intelligent monitoring, and also the basis of video content analysis and understanding. A novel method is here proposed to enhance the accuracy of human behavior recognition. First, each video image is divided into five sub-regions based on the motion...

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Bibliographic Details
Published in2016 Chinese Control and Decision Conference (CCDC) pp. 113 - 118
Main Authors Cheng, Yanyun, Zhu, Songhao, Liang, Zhiwei, Xu, Guozheng
Format Conference Proceeding Journal Article
LanguageEnglish
Published IEEE 01.05.2016
Subjects
Adaptive optics
Balanced Binary Tree
Behavior Recognition
Classification algorithms
Computer vision
Conferences
Frequency-domain analysis
Human behavior
Image motion analysis
Image Segmentation
Mathematical analysis
Mathematical model
Monitoring
Moving object recognition
Optical Flow
Optical imaging
Recognition
Support Vector Machine
Tasks
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Summary:Action recognition is one of the core content of intelligent monitoring, and also the basis of video content analysis and understanding. A novel method is here proposed to enhance the accuracy of human behavior recognition. First, each video image is divided into five sub-regions based on the motion mechanism; then, the frequency information of optical flow within each sub-region is extracted to describe the motion characteristics of each sub-region; finally, an improved balanced binary decision tree-support vector machine is utilized to complete the task of behavior recognition. Experimental results conducted on KTH database demonstrate the proposed algorithm can improve the accuracy of behavior recognition.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Conference-1
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SourceType-Conference Papers & Proceedings-2
ISSN:1948-9447
DOI:10.1109/CCDC.2016.7530964