Measuring the Road Traffic Intensity using Neural Network with Computer Vision

Abstract

Traffic congestion plagues all driver around the world. To solve this problem computer vision can be used as a tool to develop alternative routes and eliminate traffic congestions. In the current generation with increasing number of cameras on the streets and lower cost for Internet of Things(IoT) this solution will have a greater impact on current systems. In this paper, the Macroscopic Urban Traffic model is used using computer vision as its source and traffic intensity monitoring system is implemented. The input of this program is extracted from a traffic surveillance camera and another program running a neural network classification which can classify and distinguish the vehicle type is on the road. The neural network toolbox is trained with positive and negative input to increase accuracy. The accuracy of the program is compared to other related works done and the trends of the traffic intensity from a road is also calculated.

Identifikasi Tanda Tangan menggunakan Metode Fitur Ekstrasi Biner dan K Nearest Neighbor

“…Hasil yang dihasilkan mempresentasikan MCYT pada database tanda tangan [9]. Tingkat keabuan pada stabilitas fitur terhadap perubahan distribusi dari goresan tanda tangan [10]. Model yang digunakan untuk percampuran latar belakang pada perkalian gambar tanda tangan.…”