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ARTICLE

Tracking Pedestrians Under Occlusion in Parking Space

Zhengshu Zhou1,*, Shunya Yamada2, Yousuke Watanabe2, Hiroaki Takada1,2
1 Graduate School of Informatics, Nagoya University, Nagoya, 464-8601, Japan
2 Institute of Innovation for Future Society, Nagoya University, Nagoya, Aichi, Japan
* Corresponding Author: Zhengshu Zhou. Email:

Computer Systems Science and Engineering 2023, 44(3), 2109-2127. https://doi.org/10.32604/csse.2023.029005

Received 22 February 2022; Accepted 25 March 2022; Issue published 01 August 2022

Abstract

Many traffic accidents occur in parking lots. One of the serious safety risks is vehicle-pedestrian conflict. Moreover, with the increasing development of automatic driving and parking technology, parking safety has received significant attention from vehicle safety analysts. However, pedestrian protection in parking lots still faces many challenges. For example, the physical structure of a parking lot may be complex, and dead corners would occur when the vehicle density is high. These lead to pedestrians’ sudden appearance in the vehicle’s path from an unexpected position, resulting in collision accidents in the parking lot. We advocate that besides vehicular sensing data, high-precision digital map of the parking lot, pedestrians’ smart device’s sensing data, and attribute information of pedestrians can be used to detect the position of pedestrians in the parking lot. However, this subject has not been studied and explored in existing studies. To fill this void, this paper proposes a pedestrian tracking framework integrating multiple information sources to provide pedestrian position and status information for vehicles and protect pedestrians in parking spaces. We also evaluate the proposed method through real-world experiments. The experimental results show that the proposed framework has its advantage in pedestrian attribute information extraction and positioning accuracy. It can also be used for pedestrian tracking in parking spaces.

Keywords

Pedestrian positioning; object tracking; LiDAR; attribute information; sensor fusion; trajectory prediction; Kalman filter

Cite This Article

Z. Zhou, S. Yamada, Y. Watanabe and H. Takada, "Tracking pedestrians under occlusion in parking space," Computer Systems Science and Engineering, vol. 44, no.3, pp. 2109–2127, 2023.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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