Moving vehicle detection from video sequences for Traffic Surveillance System

Abstract

In the current scenario, Intelligent Transportation Systems play a significant role in smart city platform. Automatic moving vehicle detection from video sequences is the core component of the automated traffic management system. Humans can easily detect and recognize objects from complex scenes in a flash. Translating that thought process to a machine, however, requires us to learn the art of object detection using computer vision algorithms. This paper solves the traffic issues of the urban areas with an intelligent automatic transportation system. This paper includes automatic vehicle counting with the help of blob analysis, background subtraction with the use of a dynamic autoregressive moving average model, identify the moving objects with the help of a Boundary block detection algorithm, and tracking the vehicle. This paper analyses the procedure of a video-based traffic congestion system and divides it into greying, binarisation, de-nosing, and moving target detection. The investigational results show that the planned system can provide useful information for traffic surveillance.

An Efficient Machine Learning Prediction Method for Vehicle Detection: Data Analytics Framework

“…The fundamental restriction of the algorithm is its incapacity to classify vehicle categories. Heety et al [8] presented a video analysis approach to enumerate vehicles determined on an adjustable containing box measurement to identify and monitor automobiles based on their approximated position relative to the recording device. The Area of Interest, also known as the A.O.I., is determined by designating boundaries for each outward and northbound in the photograph.…”