Body Pose and Context Information for Driver Secondary Task Detection
Distraction of the driver by secondary tasks is already dangerous while driving manually but especially in handover situations in an automated mode this can lead to critical situations. Currently, these tasks are not taken into account in most modern cars. We present a system that detects typical distracting secondary tasks in an efficient modular way. We first determine the body pose of the driver and afterwards use recurrent neuronal networks to estimate actions based on sequences of the captured body poses. Our system uses knowledge about the surroundings of the driver that is unique to the car environment. Our evaluation shows that this approach achieves better results than other state of the art systems for action recognition on our dataset.