An Image Feature-Based Method for Parking Lot Occupancy

Tatulea, Paula; Calin, Florina; Brad, Remus; Brâncovean, Lucian; Greavu, Mircea

doi:10.3390/fi11080169

Cited by 17 publications

(9 citation statements)

References 10 publications

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“…However, their approach can be used for data collection. Similarly, Tȃtulea et al [16] detected the parking spaces and identified if the parking spots are occupied or available using computer vision techniques and the camera as a sensor. In order to do that, they performed different steps, including Frame Pre-processing, Adaptive Background Subtraction, Metrics & Measurements, History Creation, Results Merging for Final Classification, and Parking Space Status.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Analysis of Machine/Deep Learning Models for Parking Space Availability Prediction

Awan

Saleem

Minerva

et al. 2020

Sensors

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Machine/Deep Learning (ML/DL) techniques have been applied to large data sets in order to extract relevant information and for making predictions. The performance and the outcomes of different ML/DL algorithms may vary depending upon the data sets being used, as well as on the suitability of algorithms to the data and the application domain under consideration. Hence, determining which ML/DL algorithm is most suitable for a specific application domain and its related data sets would be a key advantage. To respond to this need, a comparative analysis of well-known ML/DL techniques, including Multilayer Perceptron, K-Nearest Neighbors, Decision Tree, Random Forest, and Voting Classifier (or the Ensemble Learning Approach) for the prediction of parking space availability has been conducted. This comparison utilized Santander’s parking data set, initiated while working on the H2020 WISE-IoT project. The data set was used in order to evaluate the considered algorithms and to determine the one offering the best prediction. The results of this analysis show that, regardless of the data set size, the less complex algorithms like Decision Tree, Random Forest, and KNN outperform complex algorithms such as Multilayer Perceptron, in terms of higher prediction accuracy, while providing comparable information for the prediction of parking space availability. In addition, in this paper, we are providing Top-K parking space recommendations on the basis of distance between current position of vehicles and free parking spots.

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Section: Related Workmentioning

confidence: 99%

A Comparative Analysis of Machine/Deep Learning Models for Parking Space Availability Prediction

Awan

Saleem

Minerva

et al. 2020

Sensors

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“…Parking situations were estimated by applying machine learning. Research that was conducted in [41] uses video camera sensors for detecting multiple parking space occupancy. Using image processing techniques: the Histogram of oriented Gradient (HOG) descriptor, the Scale-invariant feature transform (SIFT) corner detector, and Metrics on Color Spaces YUV, HSV, and YCrCb authors achieved an accuracy rate of over 93% for parking lot occupancy detection.…”

Section: State Of the Artmentioning

confidence: 99%

Sensing Occupancy through Software: Smart Parking Proof of Concept

et al. 2020

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In order to detect the vehicle presence in parking slots, different approaches have been utilized, which range from image recognition to sensing via detection nodes. The last one is usually based on getting the presence data from one or more sensors (commonly magnetic or IR-based), controlled and processed by a micro-controller that sends the data through radio interface. Consequently, given nodes have multiple components, adequate software is required for its control and state-machine to communicate its status to the receiver. This paper presents an alternative, cost-effective beacon-based mechanism for sensing the vehicle presence. It is based on the well-known effect that, once the metallic obstacle (i.e., vehicle) is on top of the sensing node, the signal strength will be attenuated, while the same shall be recognized at the receiver side. Therefore, the signal strength change conveys the information regarding the presence. Algorithms processing signal strength change at the receiver side to estimate the presence are required due to the stochastic nature of signal strength parameters. In order to prove the concept, experimental setup based on LoRa-based parking sensors was used to gather occupancy/signal strength data. In order to extract the information of presence, the Hidden Markov Model (HMM) was employed with accuracy of up to 96%, while the Neural Network (NN) approach reaches an accuracy of up to 97%. The given approach reduces the costs of the sensor production by at least 50%.

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“…In this short communication, we propose a feasible solution for heavy goods vehicle detection. Computer Vision algorithms have been implemented for various tasks in traffic monitoring for many years, e.g., traffic sign recognition [1][2][3][4][5][6][7]; intelligent traffic light system [8]; vehicle speed monitoring [9]; traffic violation monitoring [10]; vehicle tracking [11][12][13]; vehicle classification [14][15][16][17][18][19][20][21][22][23][24][25][26]; vehicle counting system on streets and highways [27][28][29][30][31]; parking spot detection from the point of view of the car for parking assistants [32,33]; and parking spot monitoring [34][35][36][37][38][39][40][41][42][43][44][45][46][47]…”

Section: Introductionmentioning

confidence: 99%

Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5

et al. 2021

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The proper planning of rest periods in response to the availability of parking spaces at rest areas is an important issue for haulage companies as well as traffic and road administrations. We present a case study of how You Only Look Once (YOLO)v5 can be implemented to detect heavy goods vehicles at rest areas during winter to allow for the real-time prediction of parking spot occupancy. Snowy conditions and the polar night in winter typically pose some challenges for image recognition, hence we use thermal network cameras. As these images typically have a high number of overlaps and cut-offs of vehicles, we applied transfer learning to YOLOv5 to investigate whether the front cabin and the rear are suitable features for heavy goods vehicle recognition. Our results show that the trained algorithm can detect the front cabin of heavy goods vehicles with high confidence, while detecting the rear seems more difficult, especially when located far away from the camera. In conclusion, we firstly show an improvement in detecting heavy goods vehicles using their front and rear instead of the whole vehicle, when winter conditions result in challenging images with a high number of overlaps and cut-offs, and secondly, we show thermal network imaging to be promising in vehicle detection.

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An Image Feature-Based Method for Parking Lot Occupancy

Cited by 17 publications

References 10 publications

A Comparative Analysis of Machine/Deep Learning Models for Parking Space Availability Prediction

A Comparative Analysis of Machine/Deep Learning Models for Parking Space Availability Prediction

Sensing Occupancy through Software: Smart Parking Proof of Concept

Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5

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