A comparative study of color and depth features for hand gesture recognition in naturalistic driving settings
Eshed Ohn-Bar, Mohan Manubhai Trivedi · 2015
We are concerned with investigating efficient video representations for the purpose of hand gesture recognition in settings of naturalistic driving. In order to provide a common experimental setup for previously proposed space-time features, we study a color and depth naturalistic hand gesture benchmark. The dataset allows for evaluation of descriptors under settings of common self-occlusion and large illumination variation. A collection of simple and quick to extract spatio-temporal cues requiring no codebook encoding are proposed. Their effectiveness is validated on our dataset, as well as on the Cambridge hand gesture dataset, improving state-of-the-art. Finally, fusion of the modalities and various cues is studied.