mlsanityData to 7 Oct 2026

E^2TAD: An Energy-Efficient Tracking-based Action Detector

arXiv 2204.04416 ↗First posted 9 Apr 2022cs.CVcited by 1

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Abstract (arXiv)

Video action detection (spatio-temporal action localization) is usually the starting point for human-centric intelligent analysis of videos nowadays. It has high practical impacts for many applications across robotics, security, healthcare, etc. The two-stage paradigm of Faster R-CNN inspires a standard paradigm of video action detection in object detection, i.e., firstly generating person proposals and then classifying their actions. However, none of the existing solutions could provide fine-grained action detection to the "who-when-where-what" level. This paper presents a tracking-based solution to accurately and efficiently localize predefined key actions spatially (by predicting the associated target IDs and locations) and temporally (by predicting the time in exact frame indices). This solution won first place in the UAV-Video Track of 2021 Low-Power Computer Vision Challenge (LPCVC).

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 73 of its 89 references that are in the map. First posted on arXiv 9 Apr 2022.

Builds on

73 of 89 references.

References · 73

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38 more
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Citing papers per year: 2023: 1.

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  1. 2023SOAR: Scene-debiasing Open-set Action Recognitioncited by 12