Status Of Parking Spaces Research Articles

Research on Parking Space Status Recognition Method Based on Computer Vision

To improve the utilization rate of parking space resources and reduce the cost of installing and maintaining sensor recognition, this paper proposed an improved computer vision-based parking space status recognition method. The overall recognition accuracy was improved by graying the video, filtering smoothing noise reduction, image enhancement pre-processing, introducing texture feature extraction method based on LBP operator, improving the background difference method, and then, we used a perceptual hash algorithm to calculate the Hamming distance between the background image and the hash string of the current frame of the video, excluding the influence of light and pedestrian on recognition accuracy. Finally, a parking space status recognition system is developed relied on the Python environment, and parking spaces are recognized in three environmental states: with direct light, without direct light, and in rain and snow. The overall average accuracy of the experimental results was 97.2%, which verifies the accuracy of the model.

MULTISOURCE INTELLIGENT PARKING ASSISTANT

Problem searching for a parking space is time-consuming and highly relevant both in Ukraine and abroad. Time spent searching for a parking space leads to excessive traffic, more traffic jams, air pollution and increased fuel consumption. These factors also affect the daily stress levels of drivers. Due to this, the process of finding a parking space should be fast and convenient. At the same time, there has been significant development of real estate in Lviv over the past few years. Accordingly, the need for organizing the process of parking cars of residents of residential areas is growing. This paper presents the results of the development of an application for a quick and convenient search for a parking space. A review of similar software applications was conducted. Proposed solutions use various technologies to solve the problem of searching for a free parking space including IoT, sensors, machine learning for image recognition. Even though they solve the problem of searching for a free parking space, most of them can be expensive to implement, maintain, they don’t provide the ability to work with different data sources. An activity diagram of system is presented and it shows two main flows of the system: displaying the current state of parking spaces and displaying parking space by the number of the car entering the territory of the complex. System consists of three modules. The first module is responsible for working with different data sources, storing the status of parking spaces, processing requests. Image processing module is responsible for determining the occupied and free parking spaces from the image. The third module is responsible for the correct display of parking spaces and their statuses. It is also demonstrated how the application works with different data sources and how exceptions are handled. The system works correctly and has a сlear interface. The parking assistant is a great helper and significantly reduces the time required to find a free parking space.

Parking Guidance System Based on Geomagnetic Sensors and Recurrent Neural Networks

The increase of motor vehicles year by year has led to a number of parking difficulties and traffic congestion problems. The intelligent parking system can effectively alleviate the parking difficulties and has received wide attention. Geomagnetic sensors are widely used due to their low cost and easy deployment. However, traditional geomagnetic parking detection algorithms cannot cope with complex parking behaviors and have low vehicle detection performance. Therefore, in this paper, a new parking guidance system is proposed by integrating related technologies such as ZigBee, geomagnetic sensor, and RNN. With our limited knowledge, in the research branch of the parking guidance system, RNN is applied to geomagnetic vehicle detection for the first time to detect the status of parking spaces and obtain more accurate identification results of geomagnetic signals. The training data is obtained from real scenarios. It is experimentally demonstrated that our method receives 96.6% accuracy in the detection of vehicle status, which is 9% higher than the state-of-the-art method. Finally, a robust parking guidance system gets 97% accuracy.

Improved Optimal Path Finding Algorithm of Multistorey Car Park Based on MCP Protocol

To address the urgent issue of car owners wasting a lot of time because they cannot find empty parking spaces in the largely multistorey car park, the paper conceives a solution including algorithm ParkIG. This scheme uses the infrared photoelectric switch to monitor the parking space status. At the same time, we define a special communication protocol named MCP to analyze the parking space data when analyzing the data transmitted by the photoelectric switch. Algorithm ParkIG is used in finding the nearest empty parking space and optimal path planning. Based on the MCP protocol and navigation system, the algorithm ParkIG initially identifies the layer of multistorey where the car is positioned. Our algorithm ParkIG greatly reduces the time and space size of the search domain and update domain when searching for vacant parking spaces in optimal path planning and ends the algorithm running to avoid a lot of computations that are not essential when the algorithm found an optimal path from the entrance to empty of parking space. Simulation experiments show that the performance of our scheme has been significantly improved, such as the time that it takes for the car owner to search for a vacant parking space is decreased by about 25.8% and reduced the space size of search domain and update domain by 70% compared to the algorithm Dijkstra.

An IoT-Driven Vehicle Detection Method Based on Multisource Data Fusion Technology for Smart Parking Management System

Recently, with the increasing difficulty of parking in cities, the wireless vehicle detectors (WVDs) based on the Internet-of-Things (IoT) technology and magnetic sensor have been widely used to obtain parking space status data, which lay the foundation for smart parking management system (SPMS). However, there is a weak magnetic region under every vehicle where the magnetic variation is even smaller than that distorted by adjacent vehicles, which leads to the accuracy decrease of the magnetometer-based vehicle detection algorithm (MB-VDA). To improve the performance of the WVDs, an IoT-Driven vehicle detection method that combines the heterogeneous data feature of the UWB channel with that of the magnetic signal is proposed in this article. In the proposed method, the length of the propagation path and signature of the channel impulse response (CIR) with respect to the vehicles, which can be obtained from UWB modules, are introduced to achieve vehicle detection. Moreover, considering that the energy consumption of UWB modules is much higher than that of magnetic sensors, the UWB-based vehicle detection algorithm is only activated when the signature of magnetic signals cannot enable MB-VDA to output an accurate decision. Finally, the proposed method was evaluated in a commercial parking lot under different conditions. The experiments show that it has a detection accuracy of 98.81% when the sampling rate of the magnetic sensor is 1 Hz.

Self-Parking management system based on geomagnetic detection and parking lock

With the increasing number of vehicles worldwide and the increasing demand for parking spaces, effective management methods are in urgent need to improve the utilization efficiency of parking spaces. In spite of many previous studies focusing on how to detect the status of parking spaces and guide vehicles, the schemes proposed by these studies can only provide certain parking information rather than effectively manage parking spaces. In this paper, a system based on intelligent parking lock is designed to realize functions of query, reservation and management, based on which, drivers can reserve the free parking space in advance. This system can ensure that the reserved parking space is not misoccupied, which can also automatically close the parking lock and perform functions such as billing after the vehicle leaves.

An Improved Roadside Parking Space Occupancy Detection Method Based on Magnetic Sensors and Wireless Signal Strength.

Smart Parking Management Systems (SPMSs) have become a research hotspot in recent years. Many researchers are focused on vehicle detection technology for SPMS which is based on magnetic sensors. Magnetism-based wireless vehicle detectors (WVDs) integrate low-power wireless communication technology, which improves the convenience of construction and maintenance. However, the magnetic signals are not only susceptible to the adjacent vehicles, but also affected by the magnetic signal dead zone of high-chassis vehicles, resulting in a decrease in vehicle detection accuracy. In order to improve the vehicle detection accuracy of the magnetism-based WVDs, the paper introduces an RF-based vehicle detection method based on the characteristics analysis of received signal strengths (RSSs) generated by the wireless transceivers. Since wireless transceivers consume more energy than magnetic sensors, the proposed RF-based method is only activated to extract the data characteristics of RSSs to further judge the states of vehicles when the data feature of magnetic signals is not sufficient to provide accurate judgment on parking space status. The proposed method was evaluated in an actual roadside parking lot and experimental results show that when the sampling rate of magnetic sensor is 1 Hz, the vehicle detection accuracy is up to 99.62%. Moreover, compared with machine-learning-based vehicle detection method, the experimental results show that our method has achieved a good compromise between detection accuracy and power consumption.

Parking Space Status Inference Upon a Deep CNN and Multi-Task Contrastive Network With Spatial Transform

Deep learning methods, especially CNNs, have achieved many promising results in a wide range of computer vision applications. However, few studies focused on designing suitable deep learning methods for parking space status inference. As we have known, it is challenging to detect parking spaces in an outdoor environment due to dynamic lighting variations, weather changes, and perspective distortion. By off-the-shelf CNNs, lighting variations might be handled well. However, to realize a practical and robust inference system, we also need to address troublesome problems, such as parking displacements, non-unified car sizes, inter-object occlusion, and perspective distortion. These problems may become even challenging if also considering the difference of space sizes. To overcome the problems, we proposed a custom–tailored deep convolutional and contrastive network with three contributions. First, we introduced a Siamese architecture to learn the contrastive and robust feature descriptor. This helps to reduce the effects owing to the variety of inter-object occlusion. Second, we integrated a convolutional Spatial Transformer Network (STN) to adaptively transform a 3-space input patch according to vehicle sizes and parking displacement. STN also helps to overcome the perspective distortion problem. Third, a multi-task loss function was designed to train the network by simultaneously considering the accuracy of inferring the status of the target space and the semantic smoothness of high-level features. Thereby, the errors caused by inter-object occlusion could be alleviated. To verify the proposed network, we visualized the learned features and analyzed their functionality. Experiments and evaluations have shown the robustness of our system in parking status inference. The real-time system currently running in public parking lots also demonstrates the effectiveness of the proposed deep network.

Design of intelligent control system for networkable stereo garage based on microprocessor

The demand for stereo garages has been an urgent issue as the number of vehicles in China has been soaring these years. An intelligent control system for networked stereo garages is introduced here. Based on authors’ design, the movement of the parking plate is controlled by a programmable logic controller (PLC). PLC sends the parking space status, the limit switch state, and the motor running parameters to the acorn RSIC machine (ARM) control system. The ARM control system then uploads the license plate recognition result and the garage operation data to the cloud platform instantly through internal network. The cloud platform would develop corresponding control strategies based on data received from the cloud portal (website and application (APP)) and the ARM controller. The test result of the system indicates that it meets the requirements of relevant technical indicators and the actual needs of users.