On-board Robust Vehicle Detection Using Knowledge-based Features and Motion Trajectory

This paper presents a robust and efficient method for vehicle detection in dynamic traffic environments. First, two adaptive vehicle hypothesis generation methods based on shadow and vehicle wave are presented, and then we assemble these two features into vehicle hypothesis. A hypothesis verification algorithm based on vehicle motion trajectory is proposed, the on-line hypothesis verification algorithm based on vehicle motion trajectory can not only reduce the false positive alarm caused by interferences, but also handle the problem that the classifiers generated in the off-line training phase is closely related to the diversity of positive and negative samples. Quantitative analysis on both public vehicle image datasets and real-time video presents a result of 85.58% detection rate with 4.13% false positive rate. And our algorithm could run as fast as 40ms/frame on PC platform.