Traffic Video Analytic Based on Convolutional Neural Network (CNN)

This project focuses on real-time congestion detection and tracking to improve traffic safety by enabling mechanisms to improve drivers' situational awareness. With real-time congestion detection in place, systems can be implemented to send advanced warnings to drivers approaching the solution of a traffic queue. The data may also be utilized to regulate variable speed restrictions in an attempt to enhance safety or reduce traffic congestion. Real-time characteristics are crucial in the context of traffic safety in order to provide drivers with pertinent information about current traffic conditions. However, the results and methods presented in this project could be utilized offline to provide traffic authorities with information on the congestion behavior of a road system, which could be used as a guide for the development of the road system., which could be used as guidance in the development of the road system. The project involves the processing of analyzing vehicle speed, object detection in vehicle type, and vehicle count. The objectives of this project are to design a video analytic vehicle detection that can detect vehicle speed, and count. The system can be implemented to study the behavior of the road. It also can determine the consistency for the object, count and speed detection system application by using a video taken at overpass in front of Universiti Tun Hussein Onn (UTHM) with comparison with another application to approve the system's accuracy.