Vision-Based Real-Time Motion Estimation of Ground Vehicles from Aerial Imagery

Author:

Kurt Yagiz,Soken Halil Ersin

Abstract

Abstract 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.

Publisher

IOP Publishing

Subject

Computer Science Applications,History,Education

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Analyzing the Effects of Visual Based Tracking Algorithms on Classical Guidance Methods;2023 10th International Conference on Recent Advances in Air and Space Technologies (RAST);2023-06-07

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