Video-Based Parking Occupancy Detection for Smart Control System (original) (raw)
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A Novel Intelligent Image-Processing Parking Systems
Artificial Intelligence and Machine Learning
The scientific community is looking for efficient solutions to improve the quality of life in large cities because of traffic congestion, driving experience, air pollution, and energy consumption. This surge exceeds the capacity of existing transit infrastructure and parking facilities. Intelligent Parking Systems (SPS) that can accommodate short-term parking demand are a must-have for smart city development. SPS are designed to count the number of parked automobiles and identify available parking spaces. In this paper, we present a novel SPS based on real-time computer vision techniques. The proposed system provides features including: vacant parking space recognition, inappropriate parking detection, forecast of available parking spaces, and directed indicators toward various sorts of parking spaces (vacant, occupied, reserved and handicapped). Our system leverages existing video surveillance systems to capture, process image sequences, train computer models to understand and inte...
Video-based Parking-space Detection
Finding a vacant parking lot in urban areas is time-consuming and, thus, not satisfying for potential visitors or customers. Efficient car-park routing systems could support drivers to get an optimal parking lot immediately. Current systems detecting vacant parking lots are either very expensive due to hardware requirement for each parking lot or do not provide a detailed occupancy map. In this paper, we propose a video-based system for low-cost vacant parking space detection. A wide-angle lens camera is used in combination with a desktop computer. Different feature extractors and machine learning algorithms were evaluated in order to retrieve accurate state information for each of the observed parking lots. We found a combination of feature extractors and classifiers which properly solved the given task. Our final system, incorporating temporal integration, reached an accuracy of 99.8 %.
Implementation of Smart Parking System Using Image Processing
SLIIT International Conference on Engineering and Technology, 2023
In recent years, the number of vehicles in use has shown a steady increase, leading to a clear demand for larger parking areas. However, the traditional methods for detecting occupancy of slots in smart vehicle parking areas are no longer feasible due to the high cost of sensors and the need to monitor larger areas. In response to this challenge, the present study aims to propose a cost-effective, fast, and accurate solution for updating and indicating the real-time number of free parking slots in a parking area. Specifically, the proposed solution utilizes video footage from a camera as the input device and applies the YOLO v3 object detection algorithm for image processing to detect the coordinates of both parking lots and parked vehicles separately. To train and evaluate the model, we used the PKLot database as the dataset and tested the model's performance under different weather conditions. The proposed model achieved an average performance of 88.01%, with the highest performance demonstrated on sunny days and the lowest performance recorded on rainy days.
Smart Parking Solution using Camera Networks and Real-time Computer Vision
IRJET, 2022
At present, the world is seeing an unprecedented push to an electric vehicle future which is forced mainly due to the climate change concerns associated with the internal combustion engine-based cars. These traditional automobiles are significantly less efficient, as a result vehicle infrastructure around cars holds a major role in reducing the carbon footprint. Thus, this research is focused on resolving a major problem that automatically comes with cars and parking. A modern single car takes up a significant space when compared with cars in the early days. Now with most of the world's population are now living in cities, invaluable space in sprawled urban infrastructure is becoming increasingly concerning. Even the electric future won't be any help to this situation. Therefore, this is suggesting improving the existing infrastructure using AI to process the existing surveillance footage. Even though most modern cars equipped with parking sensors they are limited when making small maneuvers at low speeds and cease working as soon as the car's ignition is off. There is a lack of personalized individually serving system of surveillance which the drivers can check whether a crash had been detected associated with his or her car which is currently parked.
Proposing Real-time Parking System for Smart Cities using Two Cameras
Journal of Information Systems and Telecommunication (JIST)
Today, cars are becoming a popular means of life. This rapid development has resulted in an increasing demand for private parking. Therefore, finding a parking space in urban areas is extremely difficult for drivers. Another serious problem is that parking on the roadway has serious consequences like traffic congestion. As a result, various solutions are proposed to solve basic functions such as detecting a space or determining the position of the parking to orient the driver. In this paper, we propose a system that not only detects the space but also identifies the vehicle's identity based on their respective license plate. Our proposal system includes two cameras with two independent functions, Skyeye and LPR cameras, respectively. Skyeye module has function to detect and track vehicles while automatic license plate recognition system (ALPR) module detects and identifies license plates. Therefore, the system not only helps drivers to find suitable parking space but also manages and controls vehicles effectively for street parking. Besides, it is possible to detect offending vehicles parking on the roadway based on its identity. We also collect a set of data that correctly distributes for the context in order to increase the system's performance. The accuracy of proposal system is 99.48% that shows the feasibility of applying into real environments.
An Implementation of Vacant Parking Space Detection System
International Journal of Advance Engineering and Research Development, 2015
Now a days, finding a vacant parking space in urban areas is very time-consuming. Previous vacant parking space detection systems are very high expensive due to hardware requirement for each parking lot (like sensors). In this paper we propose low cost video-based parking space detection system. The aim o f this paper is to present an intelligent system for parking lot detection based on image processing. The system captures the images and processes and produces the information of an empty vehicle parking spaces. This system operates day and ni ght. There are many challenges during day and night time like lighting conditions, shadow effects, inter-object occlusion etc.
Cost effective Parking System Using Computer Vision
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2020
This paper presents an approach for detecting real-time parking slots which includes vision-based techniques. Traditional sensor-based systems are not cost effective as 'n' number of sensors are required for 'n' parking slots. Transmitting sensor data to central system is done by hardwiring or installing dedicated wireless system which is again costly. Our technique will overcome this problem by using camera instead of number of sensors which is expensive. For detection we are using a Convolutional Neural Networks (CNN) classifier which is custom trained. It is more robust and effective in changing light conditions and weather. The following system do not require high processing as detections are done on static images not on video stream. We have also demonstrated real-time parking scenario by constructing a small prototype which shows practical implementation of our system.
Springer Lecture Notes in Computer Science, vol. 7667, pp. 506-515, 2012.
Parking is a huge problem in densely populated areas and drivers spend a significant amount of time finding a suitable place to park their cars. A system that could show drivers the nearest available space would result in enormous savings of time, fuel, and street space. In order to achieve that, real- world periodic statistical analysis of car parking areas could help increase effi- ciency. Ideally, real-time information could also be used to create personalized suggestions to drivers, thus enabling satisficing of a wide range of possible cri- teria of optimality. We propose a system that uses a single camera for a wide- area external parking, followed by a combination of two kinds of algorithms: static image analysis of parking lot spaces using a combination of histogram classification and edge detection, and dynamic image analysis using blob analy- sis. Our system thus achieves monitoring of parking spaces and reports statistics as well as empty slots in real-time. Our results indicate that almost 90% of empty spots are reported correctly, resulting in significant savings through a highly cost-effective single-camera system which can monitor more than 100 spaces.
ParkUs: A Novel Vehicle Parking Detection System
2017
Finding on-street parking in congested urban areas is a challenging chore that most drivers worldwide dislike. Previousvehicle traffic studies have estimated that around thirty percent of vehicles travelling in inner city areas are made up ofdrivers searching for a vacant parking space. While there arehardware sensor based solutions to monitor on-street parking occupancy in real-time, instrumenting and maintainingsuch a city wide system is a substantial investment. In this paper, a novel vehicle parking activity detection method, calledParkUs, is introduced and tested with the aim to eventuallyreduce vacant car parking space search times. The systemutilises accelerometer and magnetometer sensors found in allsmartphones in order to detect parking activity within a cityenvironment. Moreover, it uses a novel sensor fusion featurecalled the Orthogonality Error Estimate (OEE). We show thatthe OEE is an excellent indicator as it’s capable of detecting parking activities with high accuracy...
A Machine Vision Detection of Unauthorized On Street Roadside Parking
IJETER, 2020
The study developed a cost-effective framework for unauthorized parking detection using a machine-vision based deep learning method. The system was introduced on a Raspberry Pi 4b using the MobileNet SSD algorithm to detect vehicles illegally parked based on the live feed received from a Pi camera. The system was introduced to monitor unauthorized parking on a specific barangay simulated-roadside-parking lot. Results of the assessment indicate that the study was capable of identifying illegally parked vehicles with an overall performance rate of 96.16% and 98.93% respectively for legally and illegally parked vehicles, with a combined test resulting in 97.56%. The study showed that the detection was robust to changes in light intensity and the presence of shadow effects in varying environmental conditions, due to the deep learning strength.