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Title: Deep Learning-based Action Detection in Untrimmed Videos: A Survey
Understanding human behavior and activity facilitates advancement of numerous real-world applications, and is critical for video analysis. Despite the progress of action recognition algorithms in trimmed videos, the majority of real-world videos are lengthy and untrimmed with sparse segments of interest. The task of temporal activity detection in untrimmed videos aims to localize the temporal boundary of actions and classify the action categories. Temporal activity detection task has been investigated in full and limited supervision settings depending on the availability of action annotations. This paper provides an extensive overview of deep learning-based algorithms to tackle temporal action detection in untrimmed videos with different supervision levels including fully-supervised, weakly-supervised, unsupervised, self-supervised, and semi-supervised. In addition, this paper reviews advances in spatio-temporal action detection where actions are localized in both temporal and spatial dimensions. Action detection in online setting is also reviewed where the goal is to detect actions in each frame without considering any future context in a live video stream. Moreover, the commonly used action detection benchmark datasets and evaluation metrics are described, and the performance of the state-of-the-art methods are compared. Finally, real-world applications of temporal action detection in untrimmed videos and a set of future directions are discussed.  more » « less
Award ID(s):
2041307
NSF-PAR ID:
10428813
Author(s) / Creator(s):
;
Date Published:
Journal Name:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume:
45
Issue:
4
ISSN:
0162-8828
Page Range / eLocation ID:
4302 - 4320
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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