Publication: A camera-based smart parking system employing low-complexity deep learning for outdoor environments
5
0
Issued Date
2019
Resource Type
File Type
application/pdf
ISSN
21570981
Other identifier(s)
2-s2.0-85078963825
Rights Holder(s)
มหาวิทยาลัยศรีนครินทรวิโรฒ
Bibliographic Citation
International Conference on ICT and Knowledge Engineering. Vol 2019-November
Suggested Citation
Polprasert C., Sruayiam C., Pisawongprakan P., Teravetchakarn S. A camera-based smart parking system employing low-complexity deep learning for outdoor environments. International Conference on ICT and Knowledge Engineering. Vol 2019-November. doi:10.1109/ICTKE47035.2019.8966901 Retrieved from: https://hdl.handle.net/20.500.14740/5087
Abstract
The smart parking occupancy detection system is a technology which aims to mitigate the traffic congestion problems by reducing time for drivers to look for vacancy positions in car parking lots and providing efficient parking space utilization. Several reports have shown that the smart parking system not only alleviates traffic problems but also drives business growth and economic development within that neighborhood. In this paper, we propose a computer vision-based smart parking lot occupancy detection system employing low-complexity deep neural network architecture. A smart camera system which consists of a Raspberry Pi 3 attached to a camera utilizes a reduced-complexity deep neural network model to detect vacancy positions. We train and cross-validate our model using PKLot-Val dataset and test the performance of our model using PKLot-Test and SWUpark datasets. SWUpark dataset has been created in the context of this research, accumulating visual information of parking lots at Srinakharinwirot University across several weather conditions. Through exhaustive hyperparameter tuning and stochastic gradient descent optimization, our model achieves 88% accuracy, almost 15% higher than those obtained from state-of-the-art approach. © 2019 IEEE.
Subject(s)
Cameras
Complex networks
Gradient methods
Knowledge engineering
Learning systems
Network architecture
Statistical tests
Stochastic models
Stochastic systems
Traffic congestion
Economic development
Neural network model
Occupancy detections
Smart cameras
Smart parking
Smart parking systems
State-of-the-art approach
Stochastic gradient descent
Deep neural networks
Complex networks
Gradient methods
Knowledge engineering
Learning systems
Network architecture
Statistical tests
Stochastic models
Stochastic systems
Traffic congestion
Economic development
Neural network model
Occupancy detections
Smart cameras
Smart parking
Smart parking systems
State-of-the-art approach
Stochastic gradient descent
Deep neural networks