People spend on average 15 minutes in cruising for a parking spot and finding parking lot near -by. This accounts for about 30% of the traffic flows in cities (Arnott and Inci, 2016), and contributes to traffic congestion during the peak hours. To alleviate this issue and save time and effort in finding a vacant parking space, PGI systems (Chen and Chang, 2015) have been developed. PGI systems require accurate and up-to-date information on the occupancy of parking spaces to be able to provide the users with reliable guidance to vacant spots.
The architecture of the pre-trained deep CNN consists of 5 convolutional layers having 11x11, 5x5, 3x3, 3x3 and 3x3 image kernels respectively, that stride over the whole image, pixel by pixel (except the first layer where the stride is 4 pixels) to generate 3D volumes of feature maps. The width of the first convolution layer is 64, and 256 for the rest of the layers. A max-pooling layer follows the first, second and last convolution layer. The last convolution layer is followed by three fully connected layers having 4096, 4096 and 1000 neurons respectively and the final output consists a layer of a soft-max classifier.
An image-based framework is developed in this paper for identifying parking space occupancy in outdoor environments using features extracted by a pre-trained deep CNN and their subsequent classification by an SVM classifier. The framework achieved a high accuracy of 99.7% on the training dataset, and a transfer learning accuracy of 96.6% on an independent test dataset, which indicates its suitability for mass applications in all weather conditions. The framework can potentially provide a cheap and reliable solution to the PGI systems in outdoor environments. However, there are a few challenges limiting the performance in transfer learning including the shadows of the buildings on the parking spaces, strong solar reflection from the vehicles, vehicles parked outside or in between the designated bays by the drivers and the bias of the training data used. The performance evaluation of the framework for parking occupancy detection in the night time remains a topic of future research.
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The beauty of the transfer learning is that, a framework like this can be implemented in any on-street and residential parking space without any training and can start working right from the minute of the installation. The achievable accuracy suggests the great potential of this framework for commercial use. However, for a practical PGI system, several aspects of the proposed framework can be improved. Firstly, the model was not trained or tested in low-light conditions such as night time, which may limit its accountability and make it less persuasive for future commercial use. Secondly, in practice, it should be able to detect the pre-defined areas of the parking spaces automatically rather than manually identifying the boundaries. The parking spaces can be easily be detected by integrating a framework that can detect the parking spaces automatically. Thirdly, the framework should be tested on images from real-time surveillance to examine the applicability of live camera feed for the framework. Fourthly, while training the classifier, images of vehicle types of diverse geographical regions should be used to remove any bias created due to repetitive vehicle types of a specific geographical region. Fifthly, the ambiguity caused by partial occupancy of the parking spaces can be improved by a dynamic segmentation method. Sixthly, the effect of shadow and strong solar reflection on the classification results can be reduced by radiometric pre-processing of individual image patches before extracting the features using the CNN. Lastly, the framework can be accelerated to achieve real-time performance with a low-end cheap Graphics Processing Unit (GPU) for an increased number of parking spaces. Credit-Melbourne School of Engineering