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A Vision based Vehicle Detection System

Himanshu Chandel, Sonia Vatta. Published in Pattern Recognition.

Communications on Applied Electronics
Year of Publication: 2015
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Himanshu Chandel, Sonia Vatta
10.5120/cae2015651767

Himanshu Chandel and Sonia Vatta. Article: A Vision based Vehicle Detection System. Communications on Applied Electronics 2(6):6-16, August 2015. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Himanshu Chandel and Sonia Vatta},
	title = {Article: A Vision based Vehicle Detection System},
	journal = {Communications on Applied Electronics},
	year = {2015},
	volume = {2},
	number = {6},
	pages = {6-16},
	month = {August},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

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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Keywords

Vision, texture, vehicle, car, autonomous, shape