{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T06:43:58Z","timestamp":1740120238242,"version":"3.37.3"},"reference-count":35,"publisher":"World Scientific Pub Co Pte Ltd","issue":"10","funder":[{"DOI":"10.13039\/501100004735","name":"Hunan Provincial Natural Science Foundation of China","doi-asserted-by":"crossref","award":["2017JJ2015"],"award-info":[{"award-number":["2017JJ2015"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Hunan Province Social Science Foundation","award":["16YBA049"],"award-info":[{"award-number":["16YBA049"]}]},{"name":"Hunan Provincial Education Science and Twelve Five planning issues","award":["XJK015BGD007"],"award-info":[{"award-number":["XJK015BGD007"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2020,9]]},"abstract":"<jats:p> In order to improve the accuracy of human action recognition in video and the computational efficiency of large data sets, an action recognition algorithm based on multiple features and modified deep learning model is proposed. First, the deep network pre-training process is used to learn and optimize the RBM parameters, and the deep belief nets (DBN) model is constructed through deep learning. Then, human 13 joint points and critical points of optical flow are automatically extracted by DBN model. Second, these more abstract and more effective human motion features are combined to represent human actions. Ultimately, the entire DBN network structure is fine-tuned by support vector machine (SVM) algorithm to classify human actions. We demonstrate that human 13 joint points and critical points of optical flow are two very effective human action characterizations, our proposed approach greatly reduces the required samples, and shortens the training time of the samples, can efficiently process large data sets and can effectively recognize novel actions. We performed experiments on the KTH data set, Weizmann data set, the ballet data set and UCF101 data set to evaluate the proposed method, the experiment results show that the average recognition accuracy is over 98%, which validates its effectiveness, and show that our results are stable, reliable, and significantly better than the results of two state-of-the-art approaches on four different data sets. So, it lays a good theoretical foundation for practical applications. <\/jats:p>","DOI":"10.1142\/s0218001420550228","type":"journal-article","created":{"date-parts":[[2019,11,13]],"date-time":"2019-11-13T08:01:34Z","timestamp":1573632094000},"page":"2055022","source":"Crossref","is-referenced-by-count":3,"title":["Human Action Recognition Based on Multiple Features and Modified Deep Learning Model"],"prefix":"10.1142","volume":"34","author":[{"given":"Shaoping","family":"Zhu","sequence":"first","affiliation":[{"name":"Department of Electronic Information Engineering, Zhuhai City Polytechnic, Jinwan, Guangdong 519090, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6613-308X","authenticated-orcid":false,"given":"Yongliang","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Information Technology and Management, Hunan University of Finance and Economics, Changsha, Hunan 410205, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weimin","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Electronic Information Engineering, Zhuhai City Polytechnic, Jinwan, Guangdong 519090, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2020,1,10]]},"reference":[{"doi-asserted-by":"publisher","key":"S0218001420550228BIB001","DOI":"10.1109\/34.541407"},{"key":"S0218001420550228BIB002","first-page":"1395","volume-title":"Proc. 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