Optical Flow Based Detection and Tracking of Moving Objects for Autonomous Vehicles
<|reference_start|>Optical Flow Based Detection and Tracking of Moving Objects for Autonomous Vehicles: Accurate velocity estimation of surrounding moving objects and their trajectories are critical elements of perception systems in Automated/Autonomous Vehicles (AVs) with a direct impact on their safety. These are non-trivial problems due to the diverse types and sizes of such objects and their dynamic and random behaviour. Recent point cloud based solutions often use Iterative Closest Point (ICP) techniques, which are known to have certain limitations. For example, their computational costs are high due to their iterative nature, and their estimation error often deteriorates as the relative velocities of the target objects increase (>2 m/sec). Motivated by such shortcomings, this paper first proposes a novel Detection and Tracking of Moving Objects (DATMO) for AVs based on an optical flow technique, which is proven to be computationally efficient and highly accurate for such problems. \textcolor{black}{This is achieved by representing the driving scenario as a vector field and applying vector calculus theories to ensure spatiotemporal continuity.} We also report the results of a comprehensive performance evaluation of the proposed DATMO technique, carried out in this study using synthetic and real-world data. The results of this study demonstrate the superiority of the proposed technique, compared to the DATMO techniques in the literature, in terms of estimation accuracy and processing time in a wide range of relative velocities of moving objects. Finally, we evaluate and discuss the sensitivity of the estimation error of the proposed DATMO technique to various system and environmental parameters, as well as the relative velocities of the moving objects.<|reference_end|>
@article{sormoli2024optical,
    title={Optical Flow Based Detection and Tracking of Moving Objects for
  Autonomous Vehicles},
    author={MReza Alipour Sormoli, Mehrdad Dianati, Sajjad Mozaffari, Roger
  woodman},
    journal={arXiv preprint arXiv:2403.17779},
    year={2024},
    doi={10.1109/TITS.2024.3382495},
    archivePrefix={arXiv},
    eprint={2403.17779},
    primaryClass={cs.RO cs.SY eess.SY}
}