Micro-gesture Online Recognition using Learnable Query Points
Pengyu Liu, Fei Wang, Kun Li, Guoliang Chen, Yanyan Wei, Shengeng, Tang, Zhiliang Wu, Dan Guo
TL;DR
This paper presents a novel online micro-gesture recognition method using learnable query points, achieving high accuracy in a competitive challenge by accurately identifying gesture categories and timings.
Contribution
The authors introduce a learnable query point approach tailored for micro-gesture recognition, advancing the state-of-the-art in online gesture detection.
Findings
Ranked 2nd in the IJCAI 2024 challenge
Effective micro-gesture localization and classification
Improved detection accuracy over existing methods
Abstract
In this paper, we briefly introduce the solution developed by our team, HFUT-VUT, for the Micro-gesture Online Recognition track in the MiGA challenge at IJCAI 2024. The Micro-gesture Online Recognition task involves identifying the category and locating the start and end times of micro-gestures in video clips. Compared to the typical Temporal Action Detection task, the Micro-gesture Online Recognition task focuses more on distinguishing between micro-gestures and pinpointing the start and end times of actions. Our solution ranks 2nd in the Micro-gesture Online Recognition track.
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Taxonomy
TopicsHand Gesture Recognition Systems · Human Pose and Action Recognition · Human Motion and Animation