Aggregation of Motion Features of                         Multiple Paths for Micro-Expression                     Recognition

She, Wenxiang; Liu, Bin; Tao, Jianhua; Zhang, Hao; Lyu, Zhao

doi:10.3724/sp.j.1089.2021.18725

Cited by 3 publications

(1 citation statement)

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“…The reason human motion detection is constantly affected by many universities, home research and even companies is that application of motion detection technology in many fields after its maturity will not only help people's lives more attention, but also making human production and life easier for one more. For example, motion detection technology has been fully used in surveillance, human-computer interactions, motion analysis, content based on video retrieval, and data preserves intangible culture [4]. Human behavior analysis and feature extraction is a key topic in video retrieval research based on human behavior, and its development is shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Dance somersault gesture recognition based on multi-scale depth feature fusion

Huang¹

2023

Second Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022)

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In order to explore the problem of dance somersault gesture recognition, a kind of dance somersault gesture recognition based on multi-scale depth feature fusion is proposed. Methods Through the information recommendation of key technical problems and solutions based on multi-scale depth feature fusion, the research of dance somersault gesture recognition was explored. The research shows that the efficiency of dance somersault gesture recognition based on multi-scale depth feature fusion is about 4.6% higher than that of traditional methods. The acquisition of main video information has always been inclined to obtain video key frames. However, in the face of videos with strong continuity and low repetition between human posture sequences in various movements, only key frames can't represent all the effective information of the videos. Most algorithms excessively pursue the differences between action categories, thus ignoring the degree of "cohesion" between simple actions within actions.

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Section: Introductionmentioning

confidence: 99%