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Multi-object tracking is a well known problem in the context of vehicle tracking. Kalman filter is a common tool to solve the problem in real world. In a driving environment, there are other parameters affecting the behavior of the driver than itself such as other driver’s behavior and the environment including obstacles and possible paths. Interactive Kalman filter (IKF), a generalized from of DKF, was previously introduced to model the interaction between vehicles. To augment KF, DKF, and IKF, we use information extracted from history of traffic in the same environment called situation assessment. In this paper, we proposed SAIKF, a variant of Kalman filter and interactive Kalman filter that employs situation assessment information to enhance the performance of tracking. A graph called Motion History Graph is constructed based on the history of the vehicle motions and is then used to augment the estimation. The results on real world video sequences show effective performance improvement. [ABSTRACT FROM AUTHOR]

Kalman filter has been successfully applied to tracking moving objects in real-time situations. However, the filter cannot take into account the existing prior knowledge to improve its predictions. In the moving object tracking, the trajectories of multiple targets in the same environment could be available, which can be viewed as the prior knowledge for the tracking procedure. This paper presents the probabilistic Kalman filter (PKF) that is able to take into account the stored trajectories to improve tracking estimation. The PKF has an extra stage after two steps of the Kalman filter to refine the estimated position of the targets. The refinement is obtained by applying the Viterbi algorithm to a probabilistic graph, that is constructed based on the observed trajectories. The graph is built in the offline situation and could be adapted in the online tracking. The proposed tracker has higher accuracy compared to the standard Kalman filter and could handle widespread problems such as occlusion. Another significant achievement of the proposed tracker is to track an object with anomalous behaviors by drawing an inference based on the constructed probabilistic graph. The PKF was applied to several manually-built videos and several other video-bases containing severe occlusions, which demonstrates a significant performance in comparison with other state-of-the-art trackers. [ABSTRACT FROM AUTHOR]