Towards Accurate State Estimation: Motion Dynamics Kalman Filter for 3D Multi-Object Tracking
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Precise 3D state estimation in multi-object tracking (MOT) is critical for self-driving cars, particularly for objects occluded. Motion modeling in the Kalman filter with a constant motion assumption is widely used in MOT methods, but it neglects the continuous changes in objects' motion caused by traffic in urban environments. Although recent research introduces a multimodel Kalman filter that incorporates multiple motion models, these approaches incur significant computational overhead from the simultaneous processing of multiple models. To this end, this work introduces a motion-dynamics Kalman filter (MD-KF) that overcomes the constant-motion assumption while preserving the singularity of the motion model. MD-KF models the changes in objects' motion over successive measurements as Gaussian distributions, and adaptively adjusts a weighted motion model to account for these variations. MD-KF consistently outperforms constant and multimodel KF across multiple datasets with a significant reduction in computation latency compared to multimodel approaches. The proposed approach demonstrates its superiority in trajectory estimation during occlusion and state estimation stability for stationary objects.