The main scope of the presented research was the development of an innovative product for the management of city parking lots. Our application will ensure the implementation of the Smart City concept by using computer vision and communication platforms, which enable the development of new integrated digital services. The use of video cameras could simplify and lower the costs of parking lot controls. In the aim of parking space detection, an aggregated decision was proposed, employing various metrics, computed over a sliding window interval provided by the camera. The history created over 20 images provides an adaptive method for background and accurate detection. The system has shown high robustness in two benchmarks, achieving a recognition rate higher than 93%. The authors of [2] are segmenting the video into frames, and from each segment, a key frame is extracted on which subsequent processing occurs, in order to reduce the complexity. When the car enters or exits the car park, the movement of the car is predicted by subtracting the key frame. Initially, the parking lot does not have any parking lines. The user will manually enter the coordinates of the parking area and the vehicle for parking. The system automatically generates virtual parking lines taking into account the size of the vehicle. A unique tag is assigned to each parking space. Once the parking is divided into virtual blocks, the system will check the existence of the car in each block. Each block is converted from gray to binary and then back to gray, in order to have the car illustrated by white pixels and the parking area by black pixels. The threshold value is calculated in each block in order to detect whether or not a car is parked there. The proposed parking space detection system in [3] consists of the following steps: preprocessing, extraction of model features, multi-class SVM recognition, and MRF-based verification. At first, the input frames are rotated to uniform axes and segmented into small patches, which include three parking spaces. The ground color of these spaces is extracted as an input element by applying a ground model. Next, the SVM multi-class is instructed to analyze and classify the patches into eight classes (statuses). Eventually, a Markov random field (MRF) is built to solve latent conflicts between two neighboring patches. In this step, the posterior probability generated by SVM is used to improve accuracy. In the paper of Jordan et al. [4], a parking occupancy classifier is used and two systems were proposed. The first system is a "bottom up" one, where the parking image is pre-segmented into individual parking space images, and a convolutional neural network (CNN) is used as a classifier, in order to decide whether it is an available or occupied space. The second system is a "bottom down" one, where the parking images without segmentation are sent to the CNN, and the number of available parking spaces is detected. The research presented by Delibaltov et al. [5] is based on modeling 3D parking spaces according to the parking lot geometry. The occupancy of the parking lot is determined using a vehicle detector and the deduced volume of each space. The pixel classification is performed using a vehicle detector on the acquired image in order to determine the probability of a pixel belonging to a vehicle. In this approach, the classifications of two object recognition algorithms (vehicle detectors) are merged: a SVM classifier and a TextonBoost (TB) discriminatory model for object classification. The paper presented in [6] describes an intelligent image detection system that captures and processes hand-drawn bullets on each parking space. The image is segmented, and thus a binary image is obtained. After acquiring the binary image, the noise is removed and the edges of the detected object are traced and morphological operations of expansion and erosion are applied. The image detection module will identify the round objects by predicting the surface and perimeter of each object. The paper presented in [7] uses a suitable and functioning system, employing ICT and IoT technologies, supporting the creation of a ubiquitous and interconnected network of citizens and organizations, sharing digital data and information via the internet of things. The IoT-based system for the surveillance of roadside loading and unloading bays or parking spaces, employs a computer vision-based roadside occupation surveillance system. In the context of parking lot monitoring, one of the first papers employing a CNN is presented in [8] . A deep learning architecture designed to run on embedded systems, such as smart cameras, is used to classify the parking spaces. As a result, the only information sent to a central server for visualization, is the binary output of the parking space classification process. The research presented in [9] , propose a two-stage optimization system in order to schedule electric vehicle charging in a parking station, from different points of view, to maximize either the operator's revenue or the number of completely charged electric vehicles. In this paper, we have presented and implemented a video detection system for parking space occupancy. The experimental results have shown remarkable accuracy achieved by the proposed system. In Section 2, the proposed algorithm for parking lot management is presented, while Section 3 refers to the experimental results. Future Internet 2019, 11, 169 3 of 17 The proposed solution is divided into two steps: the initial configuration and the preprocessing chain, as shown in Figure 1 . The initial configuration consists of a user-defined parking map based on a template image, managed offline and only once. The online preprocessing chain will then acquire the input video from the camera. Initially, the background is extracted, and, afterward, using the predefined map, each parking space is extracted and various history-based metrics and measurements are computed in order to determine the status of the parking space. Finally, the number of available and occupied parking spaces are determined.
Similarly, Tȃtulea et al. REF detected the parking spaces and identified if the parking spots are occupied or available using computer vision techniques and the camera as a sensor.
An Image Feature-Based Method for Parking Lot Occupancy
Future Internet
Future Internet
Computer Science