Gl-Pam Rgb-D Gesture Recognition

The existing approaches for RGB-D gesture recognition mainly developed their systems based on the global features extracted from full sequences, which makes them unreliable for capturing some important movements. In this paper, we propose to combine the global and local context information extracted from posture, appearance, and motion sequences. Our experimental results on a large scale RGB-D gesture dataset show that the proposed global and local contexts can complement well with each other for efficiently characterizing gestures, and thus achieve the 2nd place in the ChaLearn LAP Large-scale Isolated Gesture Recognition Challenge (Round 2).