{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:11:34Z","timestamp":1760148694128,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T00:00:00Z","timestamp":1685318400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Shandong Province of China","award":["ZR2016FP04"],"award-info":[{"award-number":["ZR2016FP04"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Human behavior recognition technology is widely adopted in intelligent surveillance, human\u2013machine interaction, video retrieval, and ambient intelligence applications. To achieve efficient and accurate human behavior recognition, a unique approach based on the hierarchical patches descriptor (HPD) and approximate locality-constrained linear coding (ALLC) algorithm is proposed. The HPD is a detailed local feature description, and ALLC is a fast coding method, which makes it more computationally efficient than some competitive feature-coding methods. Firstly, energy image species were calculated to describe human behavior in a global manner. Secondly, an HPD was constructed to describe human behaviors in detail through the spatial pyramid matching method. Finally, ALLC was employed to encode the patches of each level, and a feature coding with good structural characteristics and local sparsity smoothness was obtained for recognition. The recognition experimental results on both Weizmann and DHA datasets demonstrated that the accuracy of five energy image species combined with HPD and ALLC was relatively high, scoring 100% in motion history image (MHI), 98.77% in motion energy image (MEI), 93.28% in average motion energy image (AMEI), 94.68% in enhanced motion energy image (EMEI), and 95.62% in motion entropy image (MEnI).<\/jats:p>","DOI":"10.3390\/s23115179","type":"journal-article","created":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T02:33:27Z","timestamp":1685414007000},"page":"5179","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Human Behavior Recognition via Hierarchical Patches Descriptor and Approximate Locality-Constrained Linear Coding"],"prefix":"10.3390","volume":"23","author":[{"given":"Lina","family":"Liu","sequence":"first","affiliation":[{"name":"College of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China"},{"name":"Department of Electrical, Computer, and Software Engineering, Faculty of Engineering, The University of Auckland, 20 Symonds St, Auckland 1010, New Zealand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8450-2558","authenticated-orcid":false,"given":"Kevin I-Kai","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Electrical, Computer, and Software Engineering, Faculty of Engineering, The University of Auckland, 20 Symonds St, Auckland 1010, New Zealand"}]},{"given":"Biao","family":"Tian","sequence":"additional","affiliation":[{"name":"Science and Technology Cooperation and Exchange Center of Zouping, Zouping 256200, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1812-4285","authenticated-orcid":false,"given":"Waleed H.","family":"Abdulla","sequence":"additional","affiliation":[{"name":"Department of Electrical, Computer, and Software Engineering, Faculty of Engineering, The University of Auckland, 20 Symonds St, Auckland 1010, New Zealand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7273-7499","authenticated-orcid":false,"given":"Mingliang","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0651-4278","authenticated-orcid":false,"given":"Gwanggil","family":"Jeon","sequence":"additional","affiliation":[{"name":"College of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China"},{"name":"Department of Embedded Systems Engineering, Incheon National University, Incheon 22012, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,29]]},"reference":[{"key":"ref_1","first-page":"103531","article-title":"Deep learning and RGB-D based human action, human-human and human-object interaction recognition: A survey","volume":"86","author":"Khaire","year":"2022","journal-title":"JVCIR"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wang, Z.H., Zheng, Y.F., Liu, Z., and Li, Y.J. 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