A comparative study of color and depth features for hand gesture recognition in naturalistic driving settings

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.