CANMOT: Class-Aware Noise Modeling for Multi-Object Tracking in Autonomous Driving
CANMOT improves Kalman filter-based multi-object tracking by modeling process and measurement noise differently for each object class instead of using shared global parameters. By aligning these noise models with the object's local coordinate frame, the method significantly reduces identity switches and improves tracking accuracy compared to standard methods, though full statistical uncertainty consistency is still lacking.