# Generalized Parking Occupancy Analysis Based on Dilated Convolutional Neural Network +
## 2 Related Work  
To overcome the problem described above, there currently exist various systems, such as those proposed in [2; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23]. In some studies, the parking management system problem was addressed by using smart devices [24; 25; 26; 27]. Sensor-based systems involve detection sensors, such as ultrasonic sensors, which are installed at each parking space [11; 28; 29; 30]. [PERSON] and [PERSON] [31], stated that image-based methods can be categorized into two classes: space-driven and car-driven. Space-driven methods are focused on identifying vacant parking places rather than cars [8; 32]. For images obtained from static (such as the surveillance) cameras, the strategy most frequently applied is background subtraction [33], which assumes that the variation in the background is statistically motionless within a small interval of time. Considering this assumption does not hold for outdoor scenes, the limits of this approach are immediately clear. A more robust method was proposed in [34]. The authors used Gabor filters to train a classifier with images of unoccupied parking spaces under various lighting conditions. In the car-driven method category, algorithms are trained to recognize automobiles, which are the objects of interest. Related to this issue are a few object detection methods that were proposed in [35; 36]. Because of the perspective distortion observed in most images of parking lots, such as that shown in Figure 1, cars located at a considerable distance from the camera occupy a small area in an image, and thus, fewer details can be observed, which considerably diminishes the performance of object detection algorithms. The method introduced in [5], is based on a deep Convolutional Neural Network specifically designed for smart cameras. They achieved fairly high results and we will be comparing our results with respect to it. The method, called mAlexNet, is a version of AlexNet and offers a decentralized solution for visual parking space occupancy detection. It achieved very good results, but only for the same subset of images. In [37], the problem of occlusion of parking spaces, where one or more parking spaces can be hidden by other parked vehicles, was addressed. To solve the occlusion problem, vehicle tracking to detect the events of a car entering or exiting a parking space has been suggested. Furthermore, in addition to methods that use visual techniques and ground sensors, methods exist that use sensors installed on cars or carried by drivers. For instance, in [38], a framework for detecting parking space occupancy in which the system collaborated with in-vehicle navigation systems was proposed. In [39], a crowd-sourcing solution, leveraging sensors in smartphones, was proposed for collecting real-time parking availability data. In the context of vehicle detection, the single method of which we have knowledge that uses a CNN is that presented in [40]. It uses a multi-scale CNN to detect vehicles in high-resolution satellite images. In addition to providing a dataset, the authors of [4], addressed the problem by applying machine learning techniques. They used their dataset, which contains approximately 700,000 images of parking spaces in parking lots captured by three different cameras, to train SVM classifiers on various textural features, such as LBP, LPQ, and their variations. They additionally enhanced the detection performance by using composites of SVMs, applying simple aggregation functions, such as maximum or average, to the confidence values given by the classifiers. We will be comparing our work with respect to it. In the method presented in [41], specifically trained customized neural networks were used to ascertain parking space occupancy statuses and parking demand based on visual features extracted from images of parking spaces. The method's performance evaluation, conducted in a 24-h period and based on 126 parking spaces, showed that the classifier yielded promising results for night and day.