Deep Learning-Based Video System for Accurate and Real-Time Parking Measurement

Cai, Bill; Álvarez, Ricardo; Sit, Michelle; Ratti, Carlo

doi:10.1109/jiot.2019.2902887

Cited by 58 publications

(17 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, the sensors based smart car parking availability prediction architecture is proposed. To develop the car parking prediction system, we have adopted the architecture of the smart car parking system presented in [21]. It gathers the car parking data through a variety of parking sensor networks deployed at various car parking locations.…”

Section: Methodsmentioning

confidence: 99%

“…The authors suggested that the combination of deep learning and computer vision-based techniques are suitable for locating the availability of free parking spaces. Cai et al [21] proposed a deep learning technique with a novel vehicle filter for real-time measurement of parking lots. The authors demonstrated that the proposed system achieved significant accuracy as compared to the industry benchmark system with low cost and scalability.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

IoT Based Smart Parking System Using Deep Long Short Memory Network

Ali

Irfan

et al. 2020

Electronics

View full text Add to dashboard Cite

Traffic congestion is one of the most notable urban transport problems, as it causes high energy consumption and air pollution. Unavailability of free parking spaces is one of the major reasons for traffic jams. Congestion and parking are interrelated because searching for a free parking spot creates additional delays and increase local circulation. In the center of large cities, 10% of the traffic circulation is due to cruising, as drivers nearly spend 20 min searching for free parking space. Therefore, it is necessary to develop a parking space availability prediction system that can inform the drivers in advance about the location-wise, day-wise, and hour-wise occupancy of parking lots. In this paper, we proposed a framework based on a deep long short term memory network to predict the availability of parking space with the integration of Internet of Things (IoT), cloud technology, and sensor networks. We use the Birmingham parking sensors dataset to evaluate the performance of deep long short term memory networks. Three types of experiments are performed to predict the availability of free parking space which is based on location, days of a week, and working hours of a day. The experimental results show that the proposed model outperforms the state-of-the-art prediction models.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

IoT Based Smart Parking System Using Deep Long Short Memory Network

Ali

Irfan

et al. 2020

Electronics

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…Traffic monitoring system is one area that implemented CV technology for some tasks, e.g. intelligent traffic light system [5], vehicle counting system [6]- [9], vehicle speed monitoring [8], parking lot monitoring [10]- [12], and traffic violation monitoring [13]. Every task mentioned before is started by detecting the position of each vehicle, i.e.…”

Section: Introductionmentioning

confidence: 99%

A Simple Vehicle Counting System Using Deep Learning with YOLOv3 Model

Fachrie¹

2020

RESTI

View full text Add to dashboard Cite

Deep Learning is a popular Machine Learning algorithm that is widely used in many areas in current daily life. Its robust performance and ready-to-use frameworks and architectures enables many people to develop various Deep Learning-based software or systems to support human tasks and activities. Traffic monitoring is one area that utilizes Deep Learning for several purposes. By using cameras installed in some spots on the roads, many tasks such as vehicle counting, vehicle identification, traffic violation monitoring, vehicle speed monitoring, etc. can be realized. In this paper, we discuss a Deep Learning implementation to create a vehicle counting system without having to track the vehicles movements. To enhance the system performance and to reduce time in deploying Deep Learning architecture, hence pretrained model of YOLOv3 is used in this research due to its good performance and moderate computational time in object detection. This research aims to create a simple vehicle counting system to help human in classify and counting the vehicles that cross the street. The counting is based on four types of vehicle, i.e. car, motorcycle, bus, and truck, while previous research counts the car only. As the result, our proposed system capable to count the vehicles crossing the road based on video captured by camera with the highest accuracy of 97.72%.

show abstract

Deep Learning-Based Video System for Accurate and Real-Time Parking Measurement

Cited by 58 publications

References 18 publications

IoT Based Smart Parking System Using Deep Long Short Memory Network

IoT Based Smart Parking System Using Deep Long Short Memory Network

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

A Simple Vehicle Counting System Using Deep Learning with YOLOv3 Model

Contact Info

Product

Resources

About