Situation Assessment-Augmented Interactive Kalman Filter for Multi-Vehicle Tracking
Maryam Baradaran Khalkhali, Abedin Vahedian, Hadi Sadoghi Yazdi
Abstract
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 enviroment, 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.