A Fuzzy Approach Towards Parking Space Occupancy Detection Using Low-Quality Magnetic Sensors

2018 ◽  
pp. 374-384
Author(s):  
Renato Lopes Moura ◽  
Peter Sussner
Keyword(s):  
Magnetic Sensors ◽  
Parking Space ◽  
Fuzzy Approach ◽  
Occupancy Detection

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

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2348 ◽  
Author(s):  
Liangliang Lou ◽  
Jinyi Zhang ◽  
Yong Xiong ◽  
Yanliang Jin
Keyword(s):  
Detection Method ◽  
Dead Zone ◽  
Sampling Rate ◽  
Vehicle Detection ◽  
Magnetic Sensors ◽  
Experimental Results ◽  
Detection Accuracy ◽  
Parking Space ◽  
Wireless Transceivers ◽  
Occupancy Detection

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.

A Novel Hybrid Approach Towards Parking Space Occupancy Detection

2021 ◽  
Author(s):  
Tanvi Dhope ◽  
Pranav Chitale ◽  
Shruti Rampure ◽  
Sunil Ghane
Keyword(s):  
Hybrid Approach ◽  
Parking Space ◽  
Occupancy Detection

scholarly journals Generalized Parking Occupancy Analysis Based on Dilated Convolutional Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 277 ◽  
Author(s):  
Sherzod Nurullayev ◽  
Sang-Woong Lee
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Traffic Congestion ◽  
Parking Space ◽  
Urban Centers ◽  
Detection Systems ◽  
Perspective Distortion ◽  
Occupancy Detection ◽  
Parking Lot ◽  
Parking Space Detection

The importance of vacant parking space detection systems is increasing dramatically as the avoidance of traffic congestion and the time-consuming process of searching an empty parking space is a crucial problem for drivers in urban centers. However, the existing parking space occupancy detection systems are either hardware expensive or not well-generalized for varying images captured from different camera views. As a solution, we take advantage of an affordable visual detection method that is made possible by the fact that camera monitoring is already available in the majority of parking areas. However, the current problem is a challenging vision task because of outdoor lighting variation, perspective distortion, occlusions, different camera viewpoints, and the changes due to the various seasons of the year. To overcome these obstacles, we propose an approach based on Dilated Convolutional Neural Network specifically designed for detecting parking space occupancy in a parking lot, given only an image of a single parking spot as input. To evaluate our method and allow its comparison with previous strategies, we trained and tested it on well-known publicly available datasets, PKLot and CNRPark + EXT. In these datasets, the parking lot images are already labeled, and therefore, we did not need to label them manually. The proposed method shows more reliability than prior works especially when we test it on a completely different subset of images. Considering that in previous studies the performance of the methods was compared with well-known architecture—AlexNet, which shows a highly promising achievement, we also assessed our model in comparison with AlexNet. Our investigations showed that, in comparison with previous approaches, for the task of classifying given parking spaces as vacant or occupied, the proposed approach is more robust, stable, and well-generalized for unseen images captured from completely different camera viewpoints, which has strong indications that it would generalize effectively to other parking lots.

An anti-interference detection algorithm for parking monitoring systems

2019 ◽  
Vol 12 (4) ◽  
pp. 481-496
Author(s):  
Ani Dong ◽  
Zusheng Zhang ◽  
Jiaming Chen
Keyword(s):  
Data Fusion ◽  
Local Level ◽  
Detection Algorithm ◽  
Magnetic Sensors ◽  
Sensor Nodes ◽  
Detection Accuracy ◽  
Fusion Algorithm ◽  
Content Type ◽  
Occupancy Detection ◽  
Local Decision

Purpose Magnetic sensors have recently been proposed for parking occupancy detection. However, there has adjacent interference problem, i.e. the magnetic signal is easy to be interfered by the vehicles which are parking on adjacent spaces. The purpose of this paper is to propose a sensing algorithm to eliminate the adjacent interference. Design/methodology/approach The magnetic signals are converted to the pattern representation sequences, and the similarity is calculated using the pattern distance. The detection algorithm includes two levels: local decision and data fusion. In the local decision level, the sampled signals can be divided into three classes: vacant, occupied and uncertain. Then a collaborative decision is used to fusion the signals which belong to the uncertain class for the second level. Findings An experiment system included 60 sensor nodes that were deployed on bay parking spaces. Experiment results show that the proposed algorithm has better detection accuracy than existing algorithms. Originality/value This paper proposes a data fusion algorithm to eliminate adjacent interference. To balance the energy consumption and detection accuracy, the algorithm includes two levels: local decision and data fusion. In most of cases, the local decision can obtain the accurate detection result. Only the signals that cannot be correctly detected at the local level need data fusion operation.

A Parking Space Detection Algorithm Based on Magnetic Sensors

ICTE 2015 ◽  
2015 ◽  
Author(s):  
Xiangjun He ◽  
Dongxiu Ou ◽  
Yang Yang ◽  
Jingyi Xu
Keyword(s):  
Detection Algorithm ◽  
Magnetic Sensors ◽  
Parking Space ◽  
Parking Space Detection

scholarly journals Parking Space Tracker Using Image Visualization

2021 ◽  
pp. 396-403
Author(s):  
Prof. Pradnya Kasture ◽  
Purva Hattale ◽  
Vikrant Jangam ◽  
Shrutika Khilare ◽  
Yash Ratnaparkhi
Keyword(s):  
Image Processing ◽  
Wireless Communication ◽  
Mobile Phone ◽  
Parking Space ◽  
Image Visualization ◽  
Occupancy Detection ◽  
Parking Lot ◽  
Parking Space Detection ◽  
Modern Era

In modern era, the trouble of parking is also growing because of the growth within side the quantity of vehicles. From the closing decade, there are numerous researches took place with an goal to broaden a really perfect automated parking slot occupancy detection. There is an auto mechanism that can park vehicle automatically but it is required to detect which parking slot is available and which one is busy. In this paper propose a parking space detection using image processing. In this paper proposes parking-space occupancy detection, Visualization of free parking spaces, Parking statistics, Wireless communication, Easily available components, System will get Live-stream video of the parking lot from camera. Images are captured when a car enters or leaves the parking lot. System will also work in Mobile phone (Browser).

Sign in / Sign up

Export Citation Format

Share Document