{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T06:52:42Z","timestamp":1767423162714,"version":"3.40.5"},"reference-count":27,"publisher":"World Scientific Pub Co Pte Ltd","issue":"14","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2020,12,30]]},"abstract":"<jats:p> Vision-based behavior recognition is the analysis and recognition of human behavior in video. It has been widely used in many aspects such as multimedia information retrieval, behavior monitoring, and robot perception. This paper uses the Independent Subspace Analysis (ISA) deep network model feature extraction method, which is based on the ISA model and neural network theory, and combines data preprocessing methods, [Formula: see text]-means clustering methods, and Support Vector Machine (SVM) classifiers to achieve video classification and identification of human behavior. The ISA-based deep network model feature extraction method is an unsupervised learning method that can obtain behavior characteristics with good invariance and characterization capabilities in video human behavior. The experiment was conducted on the basis of the Hollywood2 human behavior data set. This experiment was compared with other commonly used human behavior feature extraction and recognition methods. The experimental results validated the effectiveness and advantages of this method in the classification and recognition of human behavior. <\/jats:p>","DOI":"10.1142\/s0218001420560121","type":"journal-article","created":{"date-parts":[[2020,2,17]],"date-time":"2020-02-17T10:41:36Z","timestamp":1581936096000},"page":"2056012","source":"Crossref","is-referenced-by-count":3,"title":["Video Human Behavior Recognition Based on ISA Deep Network Model"],"prefix":"10.1142","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7300-5748","authenticated-orcid":false,"given":"Xian","family":"Zhong","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, P.\u00a0R.\u00a0China"},{"name":"Hubei Key Laboratory of Transportation Internet of Things, Wuhan University, Wuhan 430072, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenxin","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Wuhan University, Wuhan 430072, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruiqi","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Can","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2020,5,18]]},"reference":[{"issue":"3","key":"S0218001420560121BIB001","first-page":"27","volume-title":"Proc. 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