Integrated position and motion tracking method for online multi-vehicle tracking-by-detection

Abstract

In this paper, we present a multi-vehicle tracking method that uses integrated position and motion tracking methods to minimize missing and false detection. No existing state-of-the-art vehicle detection method can detect all the vehicles on the road and remove all false positive alarms. Therefore, a robust tracking-by-detection algorithm is necessary to minimize the number of false positive and false negative alarms. In multi-vehicle tracking, there are three types of errors such as false negative alarms, false positive alarms, and track identity switches. False negative and false positive alarms are caused by an imperfect detection algorithm, while track identity switches are caused by measurement-to-track pair confusion. Our tracking-by-detection method minimizes these errors while processing in real-time for online application. Sparse false positive alarms are reduced by a track initialization procedure. Motion tracking with selected features can minimize false negative alarms. A data association algorithm with complementary global and local distance prevents tracks from connecting measurements incorrectly. The proposed method was evaluated and verified in challenging, real road environments. The experimental results demonstrate that our multi-vehicle tracking method remarkably reduces false positive and false negative alarms and performs better than previous methods.