Online RGB-D Gesture Recognition with Extreme Learning Machines

Gesture recognition is needed in many applications such as human-computer interaction and sign language recognition. The challenges of building an actual recognition system do not lie only in reaching an acceptable recognition accuracy but also with requirements for fast online processing. In this paper, we propose a method for online gesture recognition using RGB-D data from a Kinect sensor. Frame-level features are extracted from RGB frames and the skeletal model obtained from the depth data, and then classi fied by multiple extreme learning machines. The outputs from the classi fiers are aggregated to provide the final classi fication results for the gestures. We test our method on the ChaLearn multi-modal gesture challenge data. The results of the experiments demonstrate that the method can perform effective multi-class gesture recognition in real-time.