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).

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.

scholarly journals Car Parking Space Detection using Digital Image Processing

2021 ◽  
Vol 9 (VI) ◽  
pp. 3269-3273
Author(s):  
Amogh Deshpande
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Parking Space ◽  
Parking Lot ◽  
Parking Space Detection

: Nowadays, people are facing problems finding parking spaces available in a parking lot because of the massive rise in the occupancy of cars and the increase in urbanization. We have embedded techniques of image processing in each phase of the method. It will benefit all drivers entering a parking lot from the information given by the system about the location of parking spaces available and the number of vacant parking spaces.

scholarly journals Around View Monitoring-Based Vacant Parking Space Detection and Analysis

2019 ◽  
Vol 9 (16) ◽  
pp. 3403
Author(s):  
Chih-Ming Hsu ◽  
Jian-Yu Chen
Keyword(s):  
Detection Rate ◽  
Classification Error ◽  
Parking Space ◽  
Analysis Method ◽  
Distance Information ◽  
Robust Analysis ◽  
Reference Vehicle ◽  
Parking Lot ◽  
Parking Space Detection ◽  
Types Of Information

Accelerated urbanization and the ensuing rapid increase in urban populations led to the need for a tremendous number of parking spaces. Automated parking systems coupled with new parking lot layouts can effectively address the need. However, most automated parking systems available on the market today use ultrasonic sensors to detect vacant parking spaces. One limitation of this method is that a reference vehicle must be parked in an adjacent space, and the accuracy of distance information is highly dependent on the positioning of the reference vehicle. To overcome this limitation, an around view monitoring-based method for detecting parking spaces and algorithms analyzing the vacancy of the space are proposed in this study. The framework of the algorithm comprises two main stages: parking space detection and space occupancy classification. In addition, a highly robust analysis method is proposed to classify parking space occupancy. Two angles of view were used to detect features, classified as road or obstacle features, within the parking space. Road features were used to provide information regarding the possible vacancy of a parking space, and obstacle features were used to provide information regarding the possible occupancy of a parking space. Finally, these two types of information were integrated to determine whether a specific parking space is occupied. The experimental settings in this study consisted of three common settings: an indoor parking lot, an outdoor parking lot, and roadside parking spaces. The final tests showed that the method’s detection rate was lower in indoor settings than outdoor settings because lighting problems are severer in indoor settings than outdoor settings in around view monitoring (AVM) systems. However, the method achieved favorable detection performance overall. Furthermore, we tested and compared performance based on road features, obstacle features, and a combination of both. The results showed that integrating both types of features produced the lowest rate of classification error.

scholarly journals Preprocessed Mask RCNN for Parking Space Detection in Smart Parking Systems

2020 ◽  
Vol 13 (6) ◽  
pp. 255-265
Author(s):  
Ahmad Naufal ◽  
◽  
Chastine Fatichah ◽  
Nanik Suciati ◽  
◽  
...  
Keyword(s):  
Input Image ◽  
Video Data ◽  
Parking Space ◽  
Exposure Fusion ◽  
Second Stage ◽  
Learning Techniques ◽  
Parking Lot ◽  
Parking Space Detection ◽  
Smart Parking ◽  
Fusion Framework

This research developed a smart parking system through video data analysis using deep learning techniques that automatically determine the availability of vacant parking spaces. This system has two main stages. The first is the stage of marking the parking position on the image of a parking lot captured by the camera. This research proposes a Preprocessed Region-based Convolutional Neural Network (Mask R-CNN) to mark the parking position on the input image of a full parking lot. The preprocess that combining contrast enhancement using the Exposure Fusion framework, aims to overcome the problem of lighting variations in images taken in an open area. In the second stage, each parking position is examined whether the position is vacant or not using mAlexNet. A series of trials on images with varying light conditions indicate that the Preprocessed Mask R-CNN can improve marking the parking positions with an accuracy of Intersection over Union (IoU) reach 85.80%. The result of marking the parking position is then used in the trial of the availability of parking space on video data using mAlexNet, and achieving an accuracy of 73.73%.

Parking Information Monitoring and Management System Based on Internet of Things Technology

2015 ◽  
Vol 738-739 ◽  
pp. 229-232
Author(s):  
Gui Le Wu ◽  
Dong Song Yan
Keyword(s):  
Internet Of Things ◽  
Wireless Communication ◽  
Communication Technology ◽  
Shortest Path ◽  
Urban Transportation ◽  
The Internet ◽  
Parking Space ◽  
Information Monitoring ◽  
Parking Lot ◽  
Internet Of Things Technology

Collecting the parking information accurately and making good use of the existing parking spaces are keys to solve the problem of parking difficulty. Thus, the parking system based on internet of things technology was proposed. It used a geomagnetic sensor to collect the information of parking space and transmitted the information to the management center by using zigbee wireless communication technology. The center would release the information on the Internet. Via the website, vehicle owners could find a suitable parking space for their vehicle and know the shortest path to this parking. People can conveniently and accurately know the various information of each parking lot with the help of the system. Therefore, this system can effectively solve the problem of parking by monitoring and managing the parking information. It provides access to make good use of each parking space and improve urban transportation.

scholarly journals Automated Parking Space Allocation during Transition with both Human-Operated and Autonomous Vehicles

2021 ◽  
Vol 11 (2) ◽  
pp. 855
Author(s):  
Mingkang Wu ◽  
Haobin Jiang ◽  
Chin-An Tan
Keyword(s):  
Autonomous Vehicles ◽  
Transition Period ◽  
Search Algorithm ◽  
Parking Space ◽  
Key Factors ◽  
Comprehensive Method ◽  
Space Allocation ◽  
Shortest Distance ◽  
Parking Lot ◽  
Ranking Order

As fully automated valet parking systems are being developed, there is a transition period during which both human-operated vehicles (HVs) and autonomous vehicles (AVs) are present in the same parking infrastructure. This paper addresses the problem of allocation of a parking space to an AV without conflicting with the parking space chosen by the driver of a HV. A comprehensive assessment of the key factors that affect the preference and choice of a driver for a parking space is established by the fuzzy comprehensive method. The algorithm then generates a ranking order of the available parking spaces to first predict the driver’s choice of parking space and then allocate a space for the AV. The Floyd algorithm of shortest distance is used to determine the route for the AV to reach its parking space. The proposed allocation and search algorithm is applied to the examples of a parking lot with three designed scenarios. It is shown that parking space can be reasonably allocated for AVs.

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