  Extracting hand regions and their grasp information from images robustly in
real-time is critical for occupants' safety and in-vehicular infotainment
applications. It must however, be noted that naturalistic driving scenes suffer
from rapidly changing illumination and occlusion. This is aggravated by the
fact that hands are highly deformable objects, and change in appearance
frequently. This work addresses the task of accurately localizing driver hands
and classifying the grasp state of each hand. We use a fast ConvNet to first
detect likely hand regions. Next, a pixel-based skin classifier that takes into
account the global illumination changes is used to refine the hand detections
and remove false positives. This step generates a pixel-level mask for each
hand. Finally, we study each such masked regions and detect if the driver is
grasping the wheel, or in some cases a mobile phone. Through evaluation we
demonstrate that our method can outperform state-of-the-art pixel based hand
detectors, while running faster (at 35 fps) than other deep ConvNet based
frameworks even for grasp analysis. Hand mask cues are shown to be crucial when
analyzing a set of driver hand gestures (wheel/mobile phone grasp and no-grasp)
in naturalistic driving settings. The proposed detection and localization
pipeline hence can act as a general framework for real-time hand detection and
gesture classification.
