Motion divergence fields for dynamic hand gesture recognition
Although it is in general difficult to track articulated hand motion, exemplar-based approaches provide a robust solution for hand gesture recognition. Presumably, a rich set of dynamic hand gestures are needed for a meaningful recognition system. How to build the visual representation for the motion patterns is the key for scalable recognition. We propose a novel representation based on the divergence map of the gestural motion field, which transforms motion patterns into spatial patterns. Given the motion divergence maps, we leverage modern image feature detectors to extract salient spatial patterns, such as Maximum Stable Extremal Regions (MSER). A local descriptor is extracted from each region to capture the local motion pattern. The descriptors from gesture exemplars are subsequently indexed using a pre-trained vocabulary tree. New gestures are then matched efficiently with the database gestures with a TF-IDF scheme. Our extensive experiments on a large hand gesture database with 10 categories and 1050 video samples validate the efficacy of the extracted motion patterns for gesture recognition. The proposed approach achieves an overall recognition rate of 97.62%, while the average recognition time is only 34.53 ms.