  The use of hand gestures provides a natural alternative to cumbersome
interface devices for Human-Computer Interaction (HCI) systems. However,
real-time recognition of dynamic micro hand gestures from video streams is
challenging for in-vehicle scenarios since (i) the gestures should be performed
naturally without distracting the driver, (ii) micro hand gestures occur within
very short time intervals at spatially constrained areas, (iii) the performed
gesture should be recognized only once, and (iv) the entire architecture should
be designed lightweight as it will be deployed to an embedded system. In this
work, we propose an HCI system for dynamic recognition of driver micro hand
gestures, which can have a crucial impact in automotive sector especially for
safety related issues. For this purpose, we initially collected a dataset named
Driver Micro Hand Gestures (DriverMHG), which consists of RGB, depth and
infrared modalities. The challenges for dynamic recognition of micro hand
gestures have been addressed by proposing a lightweight convolutional neural
network (CNN) based architecture which operates online efficiently with a
sliding window approach. For the CNN model, several 3-dimensional resource
efficient networks are applied and their performances are analyzed. Online
recognition of gestures has been performed with 3D-MobileNetV2, which provided
the best offline accuracy among the applied networks with similar computational
complexities. The final architecture is deployed on a driver simulator
operating in real-time. We make DriverMHG dataset and our source code publicly
available.
