The vision-based surveillance systems are widely used in analysis traffic information due to their ease of installation and accuracy of their results. In this paper, an image processing system in real-time has been proposed to detect and classify the vehicles at intersections. This information can be used to estimate traffic density at intersections, and adjust the timing of traffic light for the next light cycle. The detection operation is performed by the background subtraction technique, the approximated median filter is used to extract and update the background, then the vehicles will be tracked in the detection area. After that the vehicle classification will be implemented by using the convolutional neural network (CNN). The system is applied to videos obtained by stationary cameras. The experiments demonstrate that this system is able to robustly detect and classify the vehicles.
A Traffic Surveillance System in Real-Time to Detect and Classify Vehicles by Using Convolutional Neural Network
2019-12-01
1042894 byte
Conference paper
Electronic Resource
English
Using Cross Entropy to Detect and Classify Network Anomalous Traffic
British Library Online Contents | 2010
|Voice-Assisted Real-Time Traffic Sign Recognition System Using Convolutional Neural Network
ArXiv | 2024
|A New Approach to Classify Drones Using a Deep Convolutional Neural Network
DOAJ | 2024
|Real-Time Traffic Sign Recognition Using Convolutional Neural Networks
Springer Verlag | 2021
|Real-time vehicles detection and traffic parameter extraction for highway surveillance
Tema Archive | 2009
|