Overview of temporal action detection based on deep learning

Author:

Hu Kai,Shen Chaowen,Wang Tianyan,Xu Keer,Xia Qingfeng,Xia Min,Cai Chengxue

Abstract

AbstractTemporal Action Detection (TAD) aims to accurately capture each action interval in an untrimmed video and to understand human actions. This paper comprehensively surveys the state-of-the-art techniques and models used for TAD task. Firstly, it conducts comprehensive research on this field through Citespace and comprehensively introduce relevant dataset. Secondly, it summarizes three types of methods, i.e., anchor-based, boundary-based, and query-based, from the design method level. Thirdly, it summarizes three types of supervised learning methods from the level of learning methods, i.e., fully supervised, weakly supervised, and unsupervised. Finally, this paper explores the current problems, and proposes prospects in TAD task.

Funder

Funding of Special Development Project of Tianchang Intelligent Equipment and Instrument Research Institute

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Reference205 articles.

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