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Visual Semantic Landmark-Based Robust Mapping and Localization for Autonomous Indoor Parking

Generalized Parking Occupancy Analysis Based on Dilated Convolutional Neural Network

Pattern Recognition and Machine Learning Lab., Gachon University, 1342 Seongnamdaero, Sujeong-gu, Seongnam 13120, Gyeonggi-do, Korea
Author to whom correspondence should be addressed.
A preliminary version of this paper has been presented in 4th International Conference on Next Generation Computing which was held in Vung tau, Vietnam, 20–23 December 2018.
Sensors 2019, 19(2), 277;
Received: 12 November 2018 / Revised: 7 January 2019 / Accepted: 8 January 2019 / Published: 11 January 2019
(This article belongs to the Special Issue Sensors Applications in Intelligent Vehicle)
The importance of vacant parking space detection systems is increasing dramatically as the avoidance of traffic congestion and the time-consuming process of searching an empty parking space is a crucial problem for drivers in urban centers. However, the existing parking space occupancy detection systems are either hardware expensive or not well-generalized for varying images captured from different camera views. As a solution, we take advantage of an affordable visual detection method that is made possible by the fact that camera monitoring is already available in the majority of parking areas. However, the current problem is a challenging vision task because of outdoor lighting variation, perspective distortion, occlusions, different camera viewpoints, and the changes due to the various seasons of the year. To overcome these obstacles, we propose an approach based on Dilated Convolutional Neural Network specifically designed for detecting parking space occupancy in a parking lot, given only an image of a single parking spot as input. To evaluate our method and allow its comparison with previous strategies, we trained and tested it on well-known publicly available datasets, PKLot and CNRPark + EXT. In these datasets, the parking lot images are already labeled, and therefore, we did not need to label them manually. The proposed method shows more reliability than prior works especially when we test it on a completely different subset of images. Considering that in previous studies the performance of the methods was compared with well-known architecture—AlexNet, which shows a highly promising achievement, we also assessed our model in comparison with AlexNet. Our investigations showed that, in comparison with previous approaches, for the task of classifying given parking spaces as vacant or occupied, the proposed approach is more robust, stable, and well-generalized for unseen images captured from completely different camera viewpoints, which has strong indications that it would generalize effectively to other parking lots. View Full-Text
Keywords: parking occupancy detection; dilated convolution; PKLot; AlexNet; CarNet parking occupancy detection; dilated convolution; PKLot; AlexNet; CarNet
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MDPI and ACS Style

Nurullayev, S.; Lee, S.-W. Generalized Parking Occupancy Analysis Based on Dilated Convolutional Neural Network. Sensors 2019, 19, 277.

AMA Style

Nurullayev S, Lee S-W. Generalized Parking Occupancy Analysis Based on Dilated Convolutional Neural Network. Sensors. 2019; 19(2):277.

Chicago/Turabian Style

Nurullayev, Sherzod, and Sang-Woong Lee. 2019. "Generalized Parking Occupancy Analysis Based on Dilated Convolutional Neural Network" Sensors 19, no. 2: 277.

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