Modeling potential dangers in car video for collision alarming
Mehmet Kılıçarslan, Jiang Yu Zheng · 2012
This work models various dangerous situations that may happen to a driving vehicle on road in probability, and determines how such events are mapped to the visual field of the camera. Depending on the motion flows detected in the camera, our algorithm will identify the potential dangers and compute the time to collision for alarming. The identification of dangerous events is based on the location-specific motion information modeled in the likelihood probability distributions. The originality of the proposed approach is at the location dependent motion modeling using the knowledge of road environment. This will link the detected motion to the potential danger directly for accident avoidance. The mechanism from visual motion to the dangerous events omits the complex shape recognition so that the system can response without delay.