Combining the Kalman Filter and Particle Filter in Object Tracking to Avoid Occlusion Problems

We propose a combination of algorithms called the Kalman particle filter (KPF) that overcomes the object tracking occlusion problem in image processing while also achieving a reasonable computation time. When object occlusion occurs while using a Kalman filter (KF), we switch to the particle filter (PF) to track the object until the system is stable, and then switch back to the KF. We compared the results of running each algorithm (KF, PF, and KPF), independently, executed 30 times; the tracking performance was evaluated using six different methods. We found that KPF successfully addressed the occlusion problem, providing accurate estimates using highly efficient operations.