Benchmarking Micro-Action Recognition: Dataset, Methods, and Applications
Micro-action is an imperceptible non-verbal behaviour characterised by low-intensity movement. It offers insights into the feelings and intentions of individuals and is important for human-oriented applications such as emotion recognition and psychological assessment. However, the identification, di...
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| Published in | IEEE transactions on circuits and systems for video technology Vol. 34; no. 7; pp. 6238 - 6252 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
01.07.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1051-8215 1558-2205 |
| DOI | 10.1109/TCSVT.2024.3358415 |
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| Abstract | Micro-action is an imperceptible non-verbal behaviour characterised by low-intensity movement. It offers insights into the feelings and intentions of individuals and is important for human-oriented applications such as emotion recognition and psychological assessment. However, the identification, differentiation, and understanding of micro-actions pose challenges due to the imperceptible and inaccessible nature of these subtle human behaviors in everyday life. In this study, we innovatively collect a new micro-action dataset designated as Micro-action-52 (MA-52), and propose a benchmark named micro-action network (MANet) for micro-action recognition (MAR) task. Uniquely, MA-52 provides the whole-body perspective including gestures, upper- and lower-limb movements, attempting to reveal comprehensive micro-action cues. In detail, MA-52 contains 52 micro-action categories along with seven body part labels, and encompasses a full array of realistic and natural micro-actions, accounting for 205 participants and 22,422 video instances collated from the psychological interviews. Based on the proposed dataset, we assess MANet and other nine prevalent action recognition methods. MANet incorporates squeeze-and-excitation (SE) and temporal shift module (TSM) into the ResNet architecture for modeling the spatiotemporal characteristics of micro-actions. Then a joint-embedding loss is designed for semantic matching between video and action labels; the loss is used to better distinguish between visually similar yet distinct micro-action categories. The extended application in emotion recognition has demonstrated one of the important values of our proposed dataset and method. In the future, further exploration of human behaviour, emotion, and psychological assessment will be conducted in depth. The dataset and source code are released at https://github.com/VUT-HFUT/Micro-Action . |
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| AbstractList | Micro-action is an imperceptible non-verbal behaviour characterised by low-intensity movement. It offers insights into the feelings and intentions of individuals and is important for human-oriented applications such as emotion recognition and psychological assessment. However, the identification, differentiation, and understanding of micro-actions pose challenges due to the imperceptible and inaccessible nature of these subtle human behaviors in everyday life. In this study, we innovatively collect a new micro-action dataset designated as Micro-action-52 (MA-52), and propose a benchmark named micro-action network (MANet) for micro-action recognition (MAR) task. Uniquely, MA-52 provides the whole-body perspective including gestures, upper- and lower-limb movements, attempting to reveal comprehensive micro-action cues. In detail, MA-52 contains 52 micro-action categories along with seven body part labels, and encompasses a full array of realistic and natural micro-actions, accounting for 205 participants and 22,422 video instances collated from the psychological interviews. Based on the proposed dataset, we assess MANet and other nine prevalent action recognition methods. MANet incorporates squeeze-and-excitation (SE) and temporal shift module (TSM) into the ResNet architecture for modeling the spatiotemporal characteristics of micro-actions. Then a joint-embedding loss is designed for semantic matching between video and action labels; the loss is used to better distinguish between visually similar yet distinct micro-action categories. The extended application in emotion recognition has demonstrated one of the important values of our proposed dataset and method. In the future, further exploration of human behaviour, emotion, and psychological assessment will be conducted in depth. The dataset and source code are released at https://github.com/VUT-HFUT/Micro-Action . |
| Author | Li, Kun Zhang, Yan Wang, Meng Hu, Bin Guo, Dan |
| Author_xml | – sequence: 1 givenname: Dan orcidid: 0000-0003-2594-254X surname: Guo fullname: Guo, Dan email: guodan@hfut.edu.cn organization: Key Laboratory of Knowledge Engineering with Big Data, Ministry of Education, and the School of Computer Science and Information Engineering, Hefei University of Technology (HFUT), Hefei, China – sequence: 2 givenname: Kun orcidid: 0000-0001-5083-2145 surname: Li fullname: Li, Kun email: kunli.hfut@gmail.com organization: School of Computer Science and Information Engineering, Hefei University of Technology (HFUT), Hefei, China – sequence: 3 givenname: Bin orcidid: 0000-0003-3514-5413 surname: Hu fullname: Hu, Bin email: bh@lzu.edu.cn organization: Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu, China – sequence: 4 givenname: Yan surname: Zhang fullname: Zhang, Yan email: yanzhang.hfut@gmail.com organization: School of Computer Science and Information Engineering, Hefei University of Technology (HFUT), Hefei, China – sequence: 5 givenname: Meng orcidid: 0000-0002-3094-7735 surname: Wang fullname: Wang, Meng email: eric.mengwang@gmail.com organization: Key Laboratory of Knowledge Engineering with Big Data, Ministry of Education, and the School of Computer Science and Information Engineering, Hefei University of Technology (HFUT), Hefei, China |
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| SubjectTerms | action analysis action recognition Activity recognition body language Body parts Datasets Emotion recognition Emotions Foot Human behavior human behavioral analysis Interviews Labels Legged locomotion Mars Micro-action Psychological assessment Psychology Semantics Source code Task analysis |
| Title | Benchmarking Micro-Action Recognition: Dataset, Methods, and Applications |
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