Human Behavior Recognition via Hierarchical Patches Descriptor and Approximate Locality-Constrained Linear Coding

Human behavior recognition technology is widely adopted in intelligent surveillance, human–machine 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) a...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 23; no. 11; p. 5179
Main Authors: Liu, Lina, Wang, Kevin I-Kai, Tian, Biao, Abdulla, Waleed H., Gao, Mingliang, Jeon, Gwanggil
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 29.05.2023
MDPI
Subjects:
Accuracy
Algorithms
approximate locality-constrained linear coding algorithm
Artificial intelligence
Behavior
Classification
Collaboration
Datasets
Deep learning
Dictionaries
Energy
energy image species
hierarchical patches descriptor
Human acts
Human behavior
human behavior recognition
Human body
Neural networks
Optimization
Pattern recognition
approximate locality-constrained linear coding algorithm
hierarchical patches descriptor
energy image species
human behavior recognition
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:Human behavior recognition technology is widely adopted in intelligent surveillance, human–machine 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).
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ISSN:1424-8220
1424-8220
DOI:10.3390/s23115179