Parking spot classification based on surround view camera system

Andy Xiao; Deep Doshi; Lihao Wang; Harsha Gorantla; Thomas Heitzmann; Peter Groth

doi:10.1117/12.2677526

4 October 2023 Parking spot classification based on surround view camera system

Andy Xiao, Deep Doshi, Lihao Wang, Harsha Gorantla, Thomas Heitzmann, Peter Groth

Proceedings Volume 12675, Applications of Machine Learning 2023; 126750H (2023) https://doi.org/10.1117/12.2677526
Event: SPIE Optical Engineering + Applications, 2023, San Diego, California, United States

Abstract

Surround-view fisheye cameras are commonly used for near-field sensing in automated driving scenarios, including urban driving and auto valet parking. Four fisheye cameras, one on each side, are sufficient to cover 360° around the vehicle capturing the entire near-field region. Based on surround view cameras, there has been much research on parking slot detection with main focus on the occupancy status in recent years, but little work on whether the free slot is compatible with the mission of the ego vehicle or not. For instance, some spots are handicap or electric vehicles accessible only. In this paper, we tackle parking spot classification based on the surround view camera system. We adapt the object detection neural network YOLOv4 with a novel polygon bounding box model that is well-suited for various shaped parking spaces, such as slanted parking slots. To the best of our knowledge, we present the first detailed study on parking spot detection and classification on fisheye cameras for auto valet parking scenarios. The results prove that our proposed classification approach is effective to distinguish between regular, electric vehicle, and handicap parking spots.

Conference Presentation

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Andy Xiao, Deep Doshi, Lihao Wang, Harsha Gorantla, Thomas Heitzmann, and Peter Groth "Parking spot classification based on surround view camera system", Proc. SPIE 12675, Applications of Machine Learning 2023, 126750H (4 October 2023); https://doi.org/10.1117/12.2677526

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