LD-ConGR: A Large RGB-D Video Dataset for Long-Distance Continuous Gesture Recognition

Dan Liu, Libo Zhang, Yanjun Wu

Abstract: Gesture recognition plays an important role in natural human-computer interaction and sign language recognition. Existing research on gesture recognition is limited to close-range interaction such as vehicle gesture control and face-to-face communication. To apply gesture recognition to long-distance interactive scenes such as meetings and smart homes, a large RGB-D video dataset LD-ConGR is established in this paper. LD-ConGR is distinguished from existing gesture datasets by its long-distance gesture collection, fine-grained annotations, and high video qual-ity. Specifically, 1) the farthest gesture provided by the LD-ConGR is captured 4m away from the camera while existing gesture datasets collect gestures within 1m from the camera; 2) besides the gesture category, the temporal segmentation of gestures and hand location are also anno-tated in LD-ConGR; 3) videos are captured at high reso-lution (1280 x 720 for color streams and 640 x 576 for depth streams) and high frame rate (30 fps). On top of the LD-ConGR, a series of experimental and studies are conducted, and the proposed gesture region estimation and key frame sampling strategies are demonstrated to be effective in dealing with long-distance gesture recognition and the uncertainty of gesture duration. The dataset and experimen-tal results presented in this paper are expected to boost the research of long-distance gesture recognition. The dataset is available at https://github.com/Diananini/LD-ConGR-CVPR2022.