Gesture Recognition in Ego-Centric Videos using Dense Trajectories and Hand Segmentation
Abstract: We present a novel method for monocular hand gesture recognition in ego-vision scenarios that deals with static and dynamic gestures and can achieve high accuracy results using a few positive samples. Specifically, we use and extend the dense trajectories approach that has been successfully introduced for action recognition. Dense features are extracted around regions selected by a new hand segmentation technique that integrates superpixel classification, temporal and spatial coherence. We extensively testour gesture recognition and segmentation algorithms on public datasets and propose a new dataset shot with a wearable camera. In addition, we demonstrate that our solution can work in near real-time on a wearable device.
Citation:
Baraldi, Lorenzo; Paci, Francesco; Serra, Giuseppe; Benini, Luca; Cucchiara, Rita "Gesture Recognition in Ego-Centric Videos using Dense Trajectories and Hand Segmentation" Computer Vision and Pattern Recognition Workshops (CVPRW), 2014 IEEE Conference on, Columbus, Ohio, pp. 702 -707 , 23-28 June 2014, 2014 DOI: 10.1109/CVPRW.2014.107
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- DOI: 10.1109/CVPRW.2014.107