This research presents vision-based maneuvering object motion estimation in case of occlusion. Unequal dimension Interactive Multiple Model (UDIMM) approach is applied to increase the motion prediction accuracy when no measurement is available. Current deep learning-based multi-object tracking algorithms cannot track the objects when an occlusion exists, even for quite a short time. The main reason is the motion model used in tracking algorithms. If the measurement coming from the object detector is not available, the prediction model propagates the Kalman Filter's motion model. In this period, the object may maneuver or accelerate/decelerate so that the tracker cannot assign the tracklets successfully. Eventually, the ID switch occurs, and object tracking gets lost. Using the UDIMM approach, track loss due to the ID switch is highly reduced compared to the simple constant velocity motion model used in the StrongSort multi-object tracker algorithm. A two-dimensional simulation environment is created to compare the ground truth data with the estimation results. Performance results of different filters are compared.