Recognition of Dynamic Hand Gestures in Long Distance using a Web-Camera for Robot Guidance
This work addresses a practical limitation in human-robot interaction by enabling gesture recognition from long distances, though it appears incremental as it builds on existing architectures.
The paper tackled the problem of limited recognition distance in dynamic hand gesture models for robot guidance, achieving effective performance up to 20 meters using a web-camera.
Dynamic gestures enable the transfer of directive information to a robot. Moreover, the ability of a robot to recognize them from a long distance makes communication more effective and practical. However, current state-of-the-art models for dynamic gestures exhibit limitations in recognition distance, typically achieving effective performance only within a few meters. In this work, we propose a model for recognizing dynamic gestures from a long distance of up to 20 meters. The model integrates the SlowFast and Transformer architectures (SFT) to effectively process and classify complex gesture sequences captured in video frames. SFT demonstrates superior performance over existing models.