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Exploring Temporal Context and Human Movement Dynamics for Online Action Detection in Videos

arXiv 2106.13967 ↗First posted 26 Jun 2021cs.CV cs.HC cs.ROcited by 0

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Abstract (arXiv)

Nowadays, the interaction between humans and robots is constantly expanding, requiring more and more human motion recognition applications to operate in real time. However, most works on temporal action detection and recognition perform these tasks in offline manner, i.e. temporally segmented videos are classified as a whole. In this paper, based on the recently proposed framework of Temporal Recurrent Networks, we explore how temporal context and human movement dynamics can be effectively employed for online action detection. Our approach uses various state-of-the-art architectures and appropriately combines the extracted features in order to improve action detection. We evaluate our method on a challenging but widely used dataset for temporal action localization, THUMOS'14. Our experiments show significant improvement over the baseline method, achieving state-of-the art results on THUMOS'14.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 43 references that are in the map. First posted on arXiv 26 Jun 2021.

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References · 18

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6 more
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