# Generalized Parking Occupancy Analysis Based on Dilated Convolutional Neural Network +
## 3 Datasets  
### PKLot Dataset  
By reason of the vast number of possible applications, parking lot status detection schemes have attracted a great deal of research attention in the last decade. However, in the computer vision field,  
Figure 1: Example for a car parking area taken from PKLot dataset [4]. the lack of datasets that can be used to assess and analyze proposed algorithms is a problem that researchers face frequently. An example of a parking area is shown in Figure 1.  
To overcome this difficulty, as mentioned above the contributors of [4], introduced the PKLot dataset, which offers an important resource for researchers and practitioners whose objective is to create outdoor parking space vacancy detection systems. As our goal is to classify parking space occupancy, the PKLot dataset is one of the most appropriate candidate resources for facilitating this task, providing images captured in two parking lots from three separate camera views. It succeeds in solving the problem regarding the lack of a common dataset, allowing future benchmarking and evaluation. This dataset contains images captured on sunny, rainy, and overcast days, and thus, we found it a very useful tool for our research purpose. The PKLot dataset contains 12,417 images of parking areas and 695,899 images of parking spaces segmented from them, which have been manually checked and labeled. All the images were captured in the parking areas of the Federal University of Parana (UFPR) and the Pontifical Catholic University of Parana (PUCPR), both located in Curitiba, Brazil. The cameras were located on the roof of the buildings to decrease the potential occlusion of adjacent cars. Moreover, such settings allow series of pictures that vary widely in terms of changing illumination caused by weather variations to be obtained. To build a more challenging dataset, in [4], images presenting difficulties, such as images of vehicles that are overexposed because of sunny conditions, images with shadows caused by trees, and images collected during heavy rain that resemble pictures captured at night because of the lack of natural light, were included in the dataset. An essential detail of this dataset is that only legal parking spaces were marked and segmented. The parking lots those are signed (delimited) with parallel yellow or white lines on the floor are considered as legal ones. As one may remark, there might be some automobiles parked in an illegal manner like in the middle of the street. The pictures were saved in JPEG color format with lossless compression in a resolution of 1280 \(\times\) 720 pixels. The pictures taken from the cameras were cropped by the authors of [4], into single parking space images, rotated and grouped. Some of the examples of occupied and vacant parking spaces are shown in Figure 2.  
All the cropped images were arranged into three subsets by the authors of [4] and named as UFPR04, UFPR05, and PUCPR. The first two subsets contain pictures of varied views of the same parking area captured from cameras located on the 4 th and 5 th floors of the UFPR building. Images captured from the 10 th floor of the administration building of PUCPR constitute the third subset. Figure 2: (**a**) example of a parking lot in PKLot dataset [4]; (**b**) example images of empty parking spaces after cropping and rotating; (**c**) example images of occupied parking spaces after cropping and rotating. Our investigations showed that UFPR04 is a more challenging subset than UFPR05 and PUCPR because it contains images with diverse obstacles and ground patterns. The subset contains 3791 images captured under the aforementioned weather conditions. To these images, a semi-automatic segmentation process was applied and they were also manually labeled. A total of 105,845 images of individual parking spaces, 43.48% occupied and 56.42% vacant, was generated during the process. A very similar report was produced for the PUCPR and UFPR05 subsets.