A HYBRID METHOD TO ENHANCE SMART TRAFFIC MANAGEMENT IN CITIES USING MACHINE LEARNING









Abstract

The foremost perturb for record metropolises in the world is traffic, which centrals enormous dissipate of time and energy, and to amplified pollution. To enhance traffic in metropolises, one of the ace step is to get precise statistics about the traffic movements in the metropolises. This conceivable accomplished by the computerized video analytics to the videos from cameras which are present all over the metropolises. The main objective of our paper is to develop the methodology for analysis of the videos from different area cameras in the cities for vehicle detection and its counting. We define resourceful implementation of high recital algorithms that can process traffic videos to deliver efficient data about traffic at a low-slung energy and power cost in this paper.


Modules


Algorithms


Software And Hardware