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Reseach Article

A Vision based Vehicle Detection System

by Himanshu Chandel, Sonia Vatta
Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 2 - Number 6
Year of Publication: 2015
Authors: Himanshu Chandel, Sonia Vatta

Himanshu Chandel, Sonia Vatta . A Vision based Vehicle Detection System. Communications on Applied Electronics. 2, 6 ( August 2015), 6-16. DOI=10.5120/cae2015651767

@article{ 10.5120/cae2015651767,
author = { Himanshu Chandel, Sonia Vatta },
title = { A Vision based Vehicle Detection System },
journal = { Communications on Applied Electronics },
issue_date = { August 2015 },
volume = { 2 },
number = { 6 },
month = { August },
year = { 2015 },
issn = { 2394-4714 },
pages = { 6-16 },
numpages = {9},
url = { },
doi = { 10.5120/cae2015651767 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2023-09-04T19:40:13.508891+05:30
%A Himanshu Chandel
%A Sonia Vatta
%T A Vision based Vehicle Detection System
%J Communications on Applied Electronics
%@ 2394-4714
%V 2
%N 6
%P 6-16
%D 2015
%I Foundation of Computer Science (FCS), NY, USA

In recent years, automotive manufacturers have equipped their vehicles with innovative Advanced Driver Assistance Systems (ADAS) to ease driving and avoid dangerous situations, such as unintended lane departures or collisions with other road users, like vehicles and pedestrians. To this end, ADAS at the cutting edge are equipped with cameras to sense the vehicle surrounding. This research work investigates the techniques for monocular vision based vehicle detection. A system that can robustly detect and track vehicles in images. The system consists of three major modules: shape analysis based on Histogram of oriented gradient (HOG) is used as the main feature descriptor, a machine learning part based on support vector machine (SVM) for vehicle verification, lastly a technique is applied for texture analysis by applying the concept of gray level co-occurrence matrix (GLCM). More specifically, we are interested in detection of cars from different camera viewpoints, diverse lightning conditions majorly images in sunlight, night, rain, normal day light, low light and further handling the occlusion. The images has been pre-processed at the first step to get the optimum results in all the conditions. Experiments have been conducted on large numbers of car images with different angles. For car images the classifier contains 4 classes of images with the combination of positive and negative images, the test and train segments. Due to length of long feature vector we have deduced it using different cell sizes for more accuracy and efficiency. Results will be presented and future work will be discussed.

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Index Terms

Computer Science
Information Sciences


Vision texture vehicle car autonomous shape