Tracking-based detection of driving distraction from vehicular interior video

Distraction during driving is a growing concern for global road safety. Different activities impertinent to driving hinder the concentration of driver on road and often cause substantial damage to life and property. For making driving safe, an algorithm is proposed in this paper that is capable of detecting distraction during driving. The proposed algorithm tracks key body parts of the driver in video captured by a front camera. Euclidean distances between the tracking trajectories of body parts are used as representative features that characterize the state of distraction or attention of a driver. The well-known K-nearest neighbor classifier is applied for detecting distraction from the features extracted from body parts. The proposed method is compared with existing methods implementing tracking-based human action identification to corroborate its improved performance.