Video-based detection and analysis of driver distraction and inattention
This paper addresses two issues for mitigating driver distraction/inattention by using novel video analysis techniques: (a) inside an ego vehicle, driver inattention is monitored through first tracking drivers face/eye region using Riemannian manifold-based particle filters, followed by recognition of dynamic eye states using PPCA (probabilistic principal component analysis) and SVM (support vector machine) classifier. Frequencies of eye blinking and eye closure are used as the indication of sleepy and warning sign is then generated for recommendation; (b) outside an ego vehicle, road traffic is also analyzed. Surrounding vehicles (in both directions) are tracked, and their states are analyzed by self-calibrated cameras using view-geometries and road information. Parameters (e.g. distance, velocity, number) of tracked vehicles are estimated on the road ground plane in the 3D world coordinate system. These pieces of information are provided for mitigating drivers inattention. The main novelties of the proposed scheme include facial geometry based eye region detection for eye closure identification, combined tracking and detection of vehicles, new formulae derived in camera self-calibration, and the hybrid system that handles both daytime and nighttime scenarios. Experiments have been conducted on video data in two different types of camera settings, i.e., captured inside and outside a vehicle. Preliminary tests have been conducted, results and performance evaluation have indicated the effectiveness of the proposed methods.