Enhancing gesture recognition with multiscale feature extraction and spatial attention
Jingpeng Lei
TL;DR
This paper presents a new gesture recognition method using multiscale features and spatial attention to improve accuracy and robustness.
Contribution
A novel approach combining multiscale feature extraction and spatial attention for enhanced gesture recognition.
Findings
The proposed method outperforms existing techniques on benchmark datasets in terms of accuracy.
Multiscale feature extraction provides a more holistic representation of gestures.
Spatial attention improves focus on relevant image regions for better discrimination.
Abstract
Gesture recognition technology is a pivotal element in human-computer interaction, enabling users to communicate with machines in a natural and intuitive manner. This paper introduces a novel approach to gesture recognition that enhances accuracy and robustness by integrating multiscale feature extraction and spatial attention mechanisms. Specifically, we have developed a multiscale feature extraction module inspired by the Inception architecture, which captures comprehensive features across various scales, providing a more holistic feature representation. Additionally, We incorporate a spatial attention mechanism that focuses on image regions most relevant to the current gesture, thereby improving the discriminative power of the features. Extensive experiments conducted on multiple benchmark datasets demonstrate that our method significantly outperforms existing gesture recognition…
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Taxonomy
TopicsHand Gesture Recognition Systems · Human Pose and Action Recognition · Gait Recognition and Analysis