时序动作单元感知的开集动作识别

doi:10.16451/j.cnki.issn1003-6059.202309004

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Abstract In open set action recognition tasks, a model is requested to identify categories within the training set accurately and reject unknown actions that never appear in the training set. Currently, most of the methods treat the action as a whole, ignoring the fact that the action can be decomposed into finer-grained action units. To address this issue, a method for temporal action unit perception based open set action recognition is proposed in this paper. Firstly, an action unit relationship module is designed to learn fine-grained features of action units, and thus the relational pattern between actions and action units is obtained. The unknown actions are identified according to the different degrees of activation of known and unknown actions on action units. Secondly, an action unit temporal module is designed to model the temporal information of action units. The temporal characteristics of action units are explored to further distinguish between known actions and unknown actions that are visually similar but confusable with each other. Finally, with comprehensive consideration of both relational patterns and temporal information of action units, the model is equipped with the capability of distinguishing known actions from unknown actions. Experimental results on three action recognition datasets demonstrate the superior performance of the proposed method.

Key words： Open Set Recognition Action Recognition Action Unit Feature Alignment Temporal Perception

Received: 05 July 2023

ZTFLH:	TP183
	TP391.41

Fund:National Natural Science Foundation of China(No.62102415, 62036012, 62236008, U21B2044, 61721004, 62072286, 62072455, 62002355), Beijing Natural Science Foundation(No.L201001), Open Research Projects of Zhejiang Labora-tory(No.2022RC0AB02)

Corresponding Authors: GAO Junyu, Ph.D., associate professor. His research interests include computer vision and multimedia computing.

About author:: YANG Kaixiang, master student. His research interests include open set action recognition.FENG Yangbo, Ph.D. candidate. Her research interests include computer vision, mu-ltimedia and action recognition. XU Changsheng, Ph.D., professor. His research interests include multimedia analysis and retrieval, pattern recognition and compu-ter vision.

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Cite this article:

YANG Kaixiang,GAO Junyu,FENG Yangbo等. Temporal Action Unit Perception Based Open Set Action Recognition[J]. Pattern Recognition and Artificial Intelligence, 2023, 36(9): 806-817.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202309004 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2023/V36/I9/806

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