Parking Occupancy Detection using Thermal Camera

Paidi, Vijay; Fleyeh, Hasan

doi:10.5220/0007726804830490

Cited by 5 publications

(3 citation statements)

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“…Additionally, Regester et al [14] proposed a pattern recognition algorithm to map the location of parking spaces from aerial images of parking lots. Paidi et al [15] aimed to identify parking occupancy in an open-air parking lot, which consists of free parking spaces using a thermal camera.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Based Parking Vacancy Detection for Smart Cities

Xu¹,

Sun²,

Fu³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Parking shortage is a major problem in modern cities. Drivers cruising in search of a parking space directly translate into frustration, traffic congestion, and excessive carbon emission.We introduce a simple and effective deep learning-based parking space notification (PSN) system to inform drivers of new parking availabilities and re-occupancy of the freed spaces. Our system is particularly designed to target areas with severe parking shortages (i.e., nearly all parking spaces are occupied), a situation that allows us to convert the problem of detecting parking vacancies into recognizing vehicles leaving from their stationary positions. Our PSN system capitalizes on a calibrated Mask R-CNN model and a unique adaptation of the IoU concept to track the changes of vehicle positions in a video stream. We evaluated PSN using videos from a CCTV camera installed at a private parking lot and publicly available YouTube videos. The PSN system successfully captured all new parking vacancies arising from leaving vehicles with no false positive detections. Prompt notification messages were sent to users via cloud messaging services.

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Section: Related Workmentioning

confidence: 99%

Deep Learning Based Parking Vacancy Detection for Smart Cities

Xu¹,

Sun²,

Fu³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…However, these two methods are designed for the monitoring image of the parking lot, where the parking slots can be fully displayed. To make the vacant parking slot detection free from any weather and light conditions, the thermal camera was utilized to detect vehicles based on emitted heat in [42]. The modified Faster RCNN was trained to detect the vacant parking slot.…”

Section: Parking Slot Occupancy Classificationmentioning

confidence: 99%

“…In the implementation process, we use the Darknet-53 pre-trained on ImageNet [47] as the feature extractor of YOLOv3-based detector and then fine-tune the ps2.0 dataset [11]. In the process of fine-tuning, the batch size is 32, the image is scaled to 416 × 416, the anchors are modified for ps2.0 dataset to [ (10,13), (28,42), (33,23), (30,61), (62, 45), (61, 199), (126, 87), (156,198)], and the learning rate starts from 0.0001 and is decayed by 10 every 45,000 steps. The Adam optimizer is used with the proposed optimization setting in [48] with [β 1 , β 2 , ε] = [0.9, 0.999, 10 −8 ].…”

Section: Head and Marking Points Of The Parking Slot Detectionmentioning

confidence: 99%

Vacant Parking Slot Detection in the Around View Image Based on Deep Learning

Cao

Yan

et al. 2020

Sensors

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Due to the complex visual environment, such as lighting variations, shadows, and limitations of vision, the accuracy of vacant parking slot detection for the park assist system (PAS) with a standalone around view monitor (AVM) needs to be improved. To address this problem, we propose a vacant parking slot detection method based on deep learning, namely VPS-Net. VPS-Net converts the vacant parking slot detection into a two-step problem, including parking slot detection and occupancy classification. In the parking slot detection stage, we propose a parking slot detection method based on YOLOv3, which combines the classification of the parking slot with the localization of marking points so that various parking slots can be directly inferred using geometric cues. In the occupancy classification stage, we design a customized network whose size of convolution kernel and number of layers are adjusted according to the characteristics of the parking slot. Experiments show that VPS-Net can detect various vacant parking slots with a precision rate of 99.63% and a recall rate of 99.31% in the ps2.0 dataset, and has a satisfying generalizability in the PSV dataset. By introducing a multi-object detection network and a classification network, VPS-Net can detect various vacant parking slots robustly.

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