Adaptive temporal compression for reduction of computational complexity in human behavior recognition

The research on video analytics especially in the area of human behavior recognition has become increasingly popular recently. It is widely applied in virtual reality, video surveillance, and video retrieval. With the advancement of deep learning algorithms and computer hardware, the conventional tw...

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Vydané v:Scientific reports Ročník 14; číslo 1; s. 10560 - 11
Hlavní autori: Huang, Haixin, Wang, Yuyao, Cai, Mingqi, Wang, Ruipeng, Wen, Feng, Hu, Xiaojie
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Nature Publishing Group UK 08.05.2024
Nature Publishing Group
Nature Portfolio
Predmet:
3D convolution
639/705/1042
639/705/117
639/705/258
Adaptive
Algorithms
Compression
Compression technology
Computer applications
Computer peripherals
Convolution
Data compression
Data Compression - methods
Deep Learning
Human Activities
Human behavior recognition
Humanities and Social Sciences
Humans
Image Processing, Computer-Assisted - methods
multidisciplinary
Pattern Recognition, Automated - methods
Science
Science (multidisciplinary)
Temporal variations
Training
Video analysis
Human behavior recognition
3D convolution
Adaptive
Compression technology
Video analysis
ISSN:2045-2322, 2045-2322
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Shrnutí:The research on video analytics especially in the area of human behavior recognition has become increasingly popular recently. It is widely applied in virtual reality, video surveillance, and video retrieval. With the advancement of deep learning algorithms and computer hardware, the conventional two-dimensional convolution technique for training video models has been replaced by three-dimensional convolution, which enables the extraction of spatio-temporal features. Specifically, the use of 3D convolution in human behavior recognition has been the subject of growing interest. However, the increased dimensionality has led to challenges such as the dramatic increase in the number of parameters, increased time complexity, and a strong dependence on GPUs for effective spatio-temporal feature extraction. The training speed can be considerably slow without the support of powerful GPU hardware. To address these issues, this study proposes an Adaptive Time Compression (ATC) module. Functioning as an independent component, ATC can be seamlessly integrated into existing architectures and achieves data compression by eliminating redundant frames within video data. The ATC module effectively reduces GPU computing load and time complexity with negligible loss of accuracy, thereby facilitating real-time human behavior recognition.
Bibliografia:ObjectType-Article-1
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-61286-x