Dynamic hand gesture recognition: An exemplar-based approach from motion divergence fields
Exemplar-based approaches for dynamic hand gesture recognition usually require a large collection of gestures to achieve high-quality performance. Efficient visual representation of the motion patterns hence is very important to offer a scalable solution for gesture recognition when the databases are large. In this paper, we propose a new visual representation for hand motions based on the motion divergence fields, which can be normalized to gray-scale images. Salient regions such as Maximum Stable Extremal Regions (MSER) are then detected on the motion divergence maps. From each detected region, a local descriptor is extracted to capture local motion patterns. We further leverage indexing techniques from image search into gesture recognition. The extracted descriptors are indexed using a pre-trained vocabulary. A new gesture sample accordingly can be efficiently matched with database gestures through a term frequency-inverse document frequency (TF-IDF) weighting scheme. We have collected a hand gesture database with 10 categories and 1050 video samples for performance evaluation and further applications. The proposed method achieves higher recognition accuracy than other state-of-the-art motion and spatio-temporal features on this database. Besides, the average recognition time of our method for each gesture sequence is only 34.53ms.
hand gesture recognition divergence fields maximum stable extremal regions term frequency-inverse document frequency (tf-idf) optical flow