# Robust Parking Block Segmentation from a Surveillance Camera Perspective
## 1 Introduction  
Parking lots are dynamic environments that brew mishaps of many sorts. A crime investigation report of the Bureau of Justice Statistics of the United States in parking garage facilities states that 9% of crimes in 2010 occurred in parking places [1]. Algorithms for simulating and controlling parking lot behavior, for example, vacant space detection, rely heavily on having parking spot and parking block areas previously marked by a human [2]. In this work, we propose to segment parking blocks in a surveillance camera image. The previously mentioned insecurity problem can then be ameliorated by a second generative algorithm (out of the scope of this work) that take benefit from the priors our proposal produces. This approach was first used for traffic scene analysis in Reference [3], by using dynamic belief (Bayesian) networks. In the same vein, Reference [4] proposes to use prior probabilities of parking block areas of these spaces to guide robot navigation. Seo [4] also provides a definition for parking blocks in terms of parking spots. We extended this definition to include also partially occluded parking spots and of arbitrary shapes onto splines and containing parking spots of different sizes or neighborhood arrangements. In brief, segmenting parking blocks means to be able to identify areas in the ground plane that belong to a series of parking blocks, automatically and with high resiliency over the particular surveillance camera view conditions. There are some challenges derived by the problem formulation by itself. Firstly, an obvious challenge is the different angles and zooms a camera could have. The combinations of the three components of the projection angle and any reasonable zoom level set by the surveillance operator generates an exponentially growing set of possible projection planes. Even though we can still apply an affine transformation to normalize into a simpler domain [5], each pose yields a slightly different set of unrelated features. Considering these changes in the point of view, the most common features can become more or less visible. Secondly, in the worst case scenario, each scene is particularly packed with a series of occlusions and shadows depending on the particular camera position. This phenomenon introduces a Bayesian noise that is impossible to overcome without lots of data samples. Third, we also have a broader range of noise sources from the camera perspective in detecting parking spots. [6] categorize them into 3 main sources:  
1. **Typical Attributes. ** Similar painted lines, other vehicles, kerbstones, non-plain floor, etc. 2. **Road Conditions. ** Partially damaged parking lines, paving, etc. 3. **Surrounding Conditions. ** Weather, illumination, light source angle, etc. We can add to this list the presence of occlusions caused by temporal entities like vehicles and pedestrians. Also (but rarer), is the usage of cameras with different settings like color resolution, in the same parking lot. For these three aforementioned reasons, solving our problem by a mere (self-)supervised algorithm would require an exponential number of samples, and thus is virtually impossible. In the past 25 years, approaches to solving this problem obviated the use of satellite perspective images available for the same surveillance camera scene. With the advent of modern public Geographic Information Systems (GIS) repositories, high resolution satellite perspective images of outdoor city components became ubiquitous. These satellite perspective images frequently include parking lots, and in some cases, they are even segmented by community members. The satellite perspective allows the following advantages:  
* Parking lots are viewed from the space. * Texture appearance prevails over other features. * The view is orthogonal. * Instances can be rotated and form a manifold with the sole condition they do not overlap. * Variable angle of parking spots relative to the road, no more than 180 degrees.