# Robust Parking Block Segmentation from a Surveillance Camera Perspective
## 1 Introduction
* Satellite images rarely have shadows and we could remove them by using histogram normalization. Furthermore, Reference [4] demonstrated that canonical parking spots are easier to detect in those images. Also, Reference [7] provides a good summary of features in the satellite realm. To take advantage of these previous findings, we propose to segment the parking blocks of the surveillance camera image by:  
1. Segment the parking blocks on the satellite image. 2. Calculate an homography between the two perspectives. 3. Translate the results of the satellite image into the surveillance camera image. The beauty of this approach is that it helps us overcome the two main challenges previously mentioned--(1) temporal and static frontal occlusions [8] (It is worth noting that **overhead occlusions** can be an emerging problem but they are compensated by a clearer shot of the overall parking block structure as we will see later) and (2) variations of the point of view [9] that thwarts a supervised training approach. See Figure 1 for an illustration of our proposal. Nowadays available datasets are not enough for training a neural network, further on we explain why is this so in the datasets section. Nonetheless, we saw this challenge as a great opportunity to make a contribution to the computer vision field for identifying human made structures from the sky. For the previously mentioned reason--and in particular--to be able to train our image segmentation model in the satellite perspective, we introduce _APKLOT_, a dataset for direct parking block segmentation from a satellite perspective. More details about APKLOT (_APKLOT_ dataset files are available on the author's Github account ([[https://github.com/langheran/APKLOT](https://github.com/langheran/APKLOT)]([https://github.com/langheran/APKLOT](https://github.com/langheran/APKLOT)), Current version: 21 July 2020) dataset will be given in Section 3. Our image segmentation model was inspired by the publicly available convolutional neural network for image segmentation implementation in dlib [10] with custom parameters. For the homography calculation step we used a custom python tool that helped us mark the correspondence points, even though this wouldn't be necessary if we had had the physical setup camera projection parameters. These steps will be explained in further detail in the following sections. Finally, we also propose an evaluation protocol based on the work in Reference [8]. Companion code for all the aforementioned tasks is also provided in the github repository. The paper is organized as follows. Section \"Related Works\" explains why this problem is important in the context of the current available related work and the specific way in which we will measure success. Section 2 describes an overview of the method, emphasizing the implemented convolutional neural network and the related works from which our intuition stems. Section 3 presents the dataset we used to train our unique approach to parking block segmentation. Section 4 proposes a set of experiments to evaluate the dataset in the face of the most influential hyperparameters proposed in the literature. It also places each result along the method steps, thus providing a clearer perspective of their contribution. Section 5 remarks on the paper's most valuable contributions and detours into further developments that can be supported by our work. Figure 1: Segmented parking blocks despite several frequent vehicle occlusions from a surveillance camera perspective.