Vision-based vehicle tracking on highway traffic using bounding-box features to extract statistical information

In this study, a new bounding-box based vehicle tracking algorithm is presented to extract statistical information in the highway traffic. A novel shaking filter and a new voting approach are employed in the vehicle detection and tracking phases to reduce camera shaking effects that cause misdetection, misclassification, and mistracking. The algorithm uses image streams captured via ordinary cameras and successfully classifies and determines the time-dependent vehicle trajectory through successive frames. The novel tracking algorithm utilizes the Euclidean distance-based similarity measure to associate the detected vehicles in successive frames, or it predicts the next state of vehicles using the linear/polynomial prediction functions obtained from the trajectory vector when the observed vehicles are disappeared from the scene due to the occlusion or illusion problems. The comparative vehicle counting results show that the proposed algorithm performs approximately 7% better than the Kalman filter-based tracker.