On Performance Evaluation of Driver Hand Detection Algorithms: Challenges, Dataset, and Metrics

Abstract

Hands are used by drivers to perform primary and secondary tasks in the car. Hence, the study of driver hands has several potential applications, from studying driver behavior and alertness analysis to infotainment and human-machine interaction features. The problem is also relevant to other domains of robotics and engineering which involve cooperation with humans. In order to study this challenging computer vision and machine learning task, our paper introduces an extensive, public, naturalistic videobased hand detection dataset in the automotive environment. The dataset highlights the challenges that may be observed in naturalistic driving settings, from different background complexities, illumination settings, users, and viewpoints. In each frame, hand bounding boxes are provided, as well as left/right, driver/passenger, and number of hands on the wheel annotations. Comparison with an existing hand detection datasets highlights the novel characteristics of the proposed dataset.