Occlusion handling in parking space detection system

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Issue Date
2020-09-01
Language
en
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Abstract
Optimised use of limited resources is the essence of any smart solutions. Optimising parking space usage is a small subset of this generalised problem. Car users waste a significant share of their prime working hours looking for an available parking spot. The solutions regarding the tracking and sharing of the availability of the parking spots have been addressed using a variety of detection methodology. Traditional methods used electromagnetic sensors which gradually evolved into computer vision-based detection systems. The robustness of the computer vision-based detection system is challenged under occlusion, and this study is an attempt to alleviate this flaw. This study proposes a deep convolution neural network-based parking space detection system capable of tackling common occlusion scenarios in the parking lot. The proposed model uses mobilenet version 2 as a backbone network of the detection model architecture and Yolo version 2 as a dynamic occlusion locator integrated into parallel detection process. This study compares the detection performance of the proposed model with two benchmark detection models (mAlexnet and VGG-F) in three types of parking scenarios: Unoccluded images scenarios, artificially induced partially occluded images scenarios and video footage from a real-parking lot. The proposed detection model outperforms both benchmark detection models in all of the scenarios.
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Faculteit der Sociale Wetenschappen