Abstract
Gesture recognition is a much studied research area which has myriad real-world applications including robotics and human-machine interaction. Current gesture recognition methods have heavily focused on isolated gestures, and existing continuous gesture recognition methods are limited by a two-stage approach where independent models are required for detection and classification, with the performance of the latter being constrained by detection performance. In contrast, we introduce a single-stage continuous gesture recognition model, that can detect and classify multiple gestures in a single video via a single model. This approach learns the natural transitions between gestures and non-gestures without the need for a pre-processing segmentation stage to detect individual gestures. To enable this, we introduce a multi-modal fusion mechanism to support the integration of important information that flows from multi-modal inputs, and is scalable to any number of modes. Additionally, we propose Unimodal Feature Mapping (UFM) and Multi-modal Feature Mapping (MFM) models to map uni-modal features and the fused multi-modal features respectively. To further enhance the performance we propose a mid-point based loss function that encourages smooth alignment between the ground truth and the prediction. We demonstrate the utility of our proposed framework which can handle variable-length input videos, and outperforms the state-of-the-art on two challenging datasets, EgoGesture, and IPN hand. Furthermore, ablative experiments show the importance of different components of the proposed framework.
Abstract (translated)
URL
https://arxiv.org/abs/2011.04945
