Robust Parking Block Segmentation from a Surveillance Camera Perspective

• Published in: Applied sciences Vol. 10; no. 15; p. 5364
• Main Authors: Hurst-Tarrab, Nisim, Chang, Leonardo, Gonzalez-Mendoza, Miguel, Hernandez-Gress, Neil
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2020
• Subjects: Cameras; Datasets; deep learning; Neural networks; parking block segmentation; parking lot dataset; satellite dataset; Surveillance; Vehicles
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app10155364

Parking block regions host dangerous behaviors that can be detected from a surveillance camera perspective. However, these regions are often occluded, subject to ground bumpiness or steep slopes, and thus they are hard to segment. Firstly, the paper proposes a pyramidal solution that takes advantage of satellite views of the same scene, based on a deep Convolutional Neural Network (CNN). Training a CNN from the surveillance camera perspective is rather impossible due to the combinatory explosion generated by multiple point-of-views. However, CNNs showed great promise on previous works over satellite images. Secondly, even though there are many datasets for occupancy detection in parking lots, none of them were designed to tackle the parking block segmentation problem directly. Given the lack of a suitable dataset, we also propose APKLOT, a dataset of roughly 7000 polygons for segmenting parking blocks from the satellite perspective and from the camera perspective. Moreover, our method achieves more than 50% intersection over union (IoU) in all the testing sets, that is, at both the satellite view and the camera view.