Video-based intelligent vehicle contextual information extraction for night conditions

Advanced warning system for vehicles is a critical issue in recent years for automobiles, especially when the number of vehicles is growing rapidly world wide. The cost down of general cameras makes it feasible to have an intelligent system of visual-based event detection in front for forward collision avoidance and mitigation. When driving at nighttime, vehicles in front are generally visible by their taillights. Therefore, in this paper, a computational system, which is referred to as the dynamic visual system, is proposed to detect and analyze the taillights of the vehicles in front in spatiotemporal domain, and then extract corresponding contextual information. Predefined critical contextual information of nearby vehicles can be used for driver-assistance systems to convey a warning. Experiment from extensive dataset shows that our proposed system can effectively extract critical contextual information under different lighting and traffic conditions, and thus prove its feasibility in real-world environments.