Monocular template-based vehicle tracking for autonomous convoy driving

This paper presents a vision-based solution for detection and tracking of convoy vehicles. Our approach is able to estimate the 3D vehicle pose, velocity and steering angle of the leader vehicles and needs template images of each vehicle. The improved template-based algorithm refers to a previously publication. First, we present an extension of our dynamic region growing algorithm, which is used to remove unnecessary image information. Thanks to a new preprocessing step, the segmentation algorithm is more stable while finding the vehicle silhouette. Second, we achieve an improved pose estimation by using rotated image features. Third, the extensive comparison of algorithms to train cascade classifiers leads to the best one for vehicle detection. The algorithm was evaluated while driving autonomously in urban and non-urban environments. Experimental validation shows that our approach can detect and track poorly visible vehicles under different weather conditions in real-time.