One-Shot Learning for Real-Time Action Recognition

The goal of the paper is to develop a one-shot real-time learning and recognition system for 3D actions. We use RGBD images, combine motion and appearance cues, and map them into a new overcomplete space. The proposed method relies on descriptors based on 3D Histogram of Flow (3DHOF) and on Global Histogram of Oriented Gradient (GHOG); adaptive sparse coding (SC) is further applied to capture high-level patterns. We add effective on-line video segmentation and finally the recognition of actions through linear SVMs. The main contribution of the paper is a real-time system for one-shot action modeling; moreover we highlight the effectiveness of sparse coding techniques to represent 3D actions. We obtain very good results on the ChaLearn Gesture Dataset and with a Kinect sensor.