Car parking vacancy detection and its application in 24-hour statistical analysis (original) (raw)

One-Day Long Statistical Analysis of Parking Demand by Using Single-Camera Vacancy Detection

Many researches have focused on parking demand to gain information for traffic management recommendations and decision-making where real-world car park statistics is of great importance. This paper seeks to obtain one-day long statistical analysis of a multipurpose off-street parking space in downtown Abu Dhabi, using a single-camera vacancy detection system. The proposed methodology to collect one-day long statistics uses pattern recognition to determine occupancy states based on visual features extracted from parking spots. This vacancy detection system has two major advantages. First, it relies on only few pixels compared with other methods, being able to cover more than 150 parking spots within a single camera frame. Second, the system works well in both nighttime and daytime – robust to changing light conditions. The accuracy is 99.9% for occupied spots and 97.9% for empty spots for this period of study. This study also proposes a better indication of parking demand when the park is near its full capacity, as the utilization rate does not capture the parking demand from the motorists who fail to find parking spaces.

Detection Of Vacant Parking Spaces Through The Use Of Convolutional Neural Network

The International FLAIRS Conference Proceedings, 2021

This paper focuses on the application of computer vision and convolutional neural network techniques in the automotive industry to reduce the amount of time required to locate a vacant parking spot and to reduce driving time. The main motivation for a vacant parking spot detector is such that today’s drivers are facing major difficulties in finding available spots in largely populated cities. This often time leads to increased congestion and frustration for the driver because they are forced to continue their search for a parking spot. Our approach is able to solve this issue and provide the driver with useful information through the use of transfer learning methodologies. The main contribution of this paper is to examine and improve on previously implemented transfer learning methods in order to better increase the detection accuracy. This paper differs from previous attempts such that it considers all environmental factors such as weather and time of day. Other models are not able...

Vacant parking space identification using probabilistic neural network

The need for public parking space is increasing nowadays due to the high number of cars available. Users of car parking services, in general, are still looking for vacant parking locations to park their vehicle manually. With the current technological developments, especially in image processing field, it is expected to solve the parking space problem. Therefore, this research implements image processing to determine the location of vacant parking space or occupied ones that run in real-time. In this study, the proposed method is divided into five stages. The first stage is image acquisition to capture the image of parking location. Then it continues to pre-processing stage which consists of the process of saturation, grayscale and thresholding. The third stage is image segmentation to cut the image into five parts. The next stage is feature extraction using invariant moment, and the last stage would be identification process to determine the location of vacant parking spaces or occupied ones. The results of this research using 100 test images generates an accuracy, recall, and precision of 94%.

Comparing Image Features and Machine Learning Algorithms for Real-Time Parking Space Classification

Computing in Civil Engineering, 2013

Finding a vacant parking lot in urban areas is mostly time-consuming and not satisfying for potential visitors or customers. Efficient car-park routing systems could support drivers to find an unoccupied parking lot. Current systems detecting vacant parking lots are either very expensive due to the hardware requirement or do not provide a detailed occupancy map. In this paper, we propose a video-based system for low-cost parking space classification. A wide-angle lens camera is used in combination with a desktop computer. We evaluate image features and machine learning algorithms to determine the occupancy of parking lots. Each combination of feature set and classifier was trained and tested on our dataset containing approximately 10,000 samples. We assessed the performance of all combinations of feature extraction and classification methods. Our final system, incorporating temporal filtering, reached an accuracy of 99.8 %.

Deep learning versus traditional methods for parking lots occupancy classification

Indonesian Journal of Electrical Engineering and Computer Science, 2020

Due to the increase in number of cars and slow city developments, there is a need for smart parking system. One of the main issues in smart parking systems is parking lot occupancy status classification, so this paper introduce two methods for parking lot classification. The first method uses the mean, after converting the colored image to grayscale, then to black/white. If the mean is greater than a given threshold it is classified as occupied, otherwise it is empty. This method gave 90% correct classification rate on cnrall database. It overcome the alexnet deep learning method trained and tested on the same database (the mean method has no training time). The second method, which depends on deep learning is a deep learning neural network consists of 11 layers, trained and tested on the same database. It gave 93% correct classification rate, when trained on cnrall and tested on the same database. As shown, this method overcome the alexnet deep learning and the mean methods on the ...

A Comprehensive Study of Real-Time Vacant Parking Space Detection Towards the need of a

AIUB Journal of Science and Engineering , 2020

Detection of vacant parking space is becoming a challenging task gradually. Space utilization and management of vehicle space is now a demandable field of research. Searching for an empty parking space in congested traffic is a time-consuming process. The existing vacant parking space detection methods are not robust or generalized for images captured from different camera viewpoints. Finding a proper parking space in a busy city is really a challenging issue and people are facing this problem on a daily basis. The main purpose of this research is to comprehensively discuss the previous researches of vacant parking space detection and compare them from different aspects. Methods used in previous researches are descriptively discussed along with their advantages and disadvantages. The frameworks of previous researches were compared on six generalized phases and the experimental results are compared in terms of dataset, accuracy, processing time and other performance measures. This research also focuses on the challenges of vision-based vacant parking space detection which will contribute to future researches and researchers can work to overcome these challenges.

A Comprehensive Study of Real-Time Vacant Parking Space Detection Towards the need of a Robust Model

AIUB Journal of Science and Engineering (AJSE), 2020

Automated Vacancy Detection in a Parking-lOT using Image Processing and Remote Access to Real-Time State

International Journal for Research in Applied Science and Engineering Technology, 2023

There has been a sudden increase in the number of vehicles in recent years. Availability of parking spaces is now become a critical issue in urban areas. Finding unoccupied space for parking vehicles has now become a challenge for drivers as it results in time wastage, loss of fuel, and increased traffic congestion. To reduce such problems, an intelligent system is required that can detect vacant parking spots in a specific parking lot. In a research paper, we present an automated vacancy detection system that uses image processing to detect the available parking spots in a parking lot. A proposed system consists of a camera that monitors the real-time state of the parking lot, an image processing unit, and a display outside the parking lot for users, showing the availability of parking spots. A system uses a vehicle detection algorithm to recognize the presence of vehicles in a parking space. The algorithm includes image binarization, dilation of an image, and edge detection. As the system identifies the available parking spaces accurately, the user display gets updated.

Parking Space Occupancy Verification - Improving Robustness using a Convolutional Neural Network

Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications

With the number of privately owned cars increasing, the issue of locating an available parking space becomes apparant. This paper deals with the verification of vacant parking spaces, by using a vision based system looking over parking areas. In particular the paper proposes a binary classifier system, based on a Convolutional Neural Network, that is capable of determining if a parking space is occupied or not. A benchmark database consisting of images captured from different parking areas, under different weather and illumination conditions, has been used to train and test the system. The system shows promising performance on the database with an accuracy of 99.71 % overall and is robust to the variations in parking areas and weather conditions.

Vacant Parking Space Detection Based on a Multilayer Inference Framework

IEEE Transactions on Circuits and Systems for Video Technology, 2017

In a practical environment, the viewing angle and height of a video surveillance camera are uncontrollable. This may cause severe inter-object occlusion and complicate the detection problem. In this paper, we proposed a novel inference framework with multiple layers forvacantparking space detection. The framework consists of an Image layer, a Patch layer, a Space layer, and a Lot layer. In the Image layer, image patches were selected based on the 3D parking lot structure. We found that the occlusion pattern within each patchrevealscuesof the parking status. Thus, our system extracted lighting-invariant features of patches and trained weak classifiers for the recognition of the occlusion pattern in the Patch layer. The outputs of the classifiers, presenting the types of inter-object occlusion, were treated as the mid-level features and inputted to the Space layer. Next, a boosted space classifier was trained to recognize the mid-level features and output the status of a 3-space unit in a probability fashion. In the Lot layer, we regarded the local status decision of 3-space units as high-level evidences and proposed a Markov Random Field to refine the parking status. In addition, we extended the framework to bridge multiple cameras and integrate the complementary information for vacant space detection.Our results show that the proposed framework can overcome the inter-object occlusion and achieve betterstatus inferencein many environmental variations and different weather conditions.We also presented a real-time system to demonstrate the computing efficiency and the system robustness.

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