Multi-Lane Detection and Road Traffic Congestion Classification for Intelligent Transportation System
Intelligent Traffic Systems have been widely used for traffic monitoring on roadway, and it is one of the most practicable tools to provide the instant road traffic information for everyone needs it, especially mobile users that demand instant information of road traffic. When traffic congestion arises, if the vehicles get the traffic information earlier, they can choose recommend alternate routes to avoid the traffic jam. Therefore, the authors propose a traffic congestion classification framework to allow classification of congestion in traffic video sequences from real-time surveillance. The framework consists of three procedures: the first one is the roadway mask with bidirectional roadway analysis, and then the virtual detectors are set up for each lane without lane marking detection.