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An improved approach for detecting car in video using neural network model

Publication Type : Journal Article

Publisher : Journal of Computer Science

Source : Journal of Computer Science, Volume 8, Number 10, p.1759-1768 (2012)

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Campus : Coimbatore

School : School of Engineering

Department : Computer Science

Verified : Yes

Year : 2012

Abstract : The study represents a novel approach taken towards car detection, feature extraction and classification in a video. Though many methods have been proposed to deal with individual features of a vehicle, like edge, license plate, corners, no system has been implemented to combine features. Combination of four unique features, namely, color, shape, number plate and logo gives the application a stronghold on various applications like surveillance recording to detect accident percentage(for every make of a company), authentication of a car in the Parliament(for high security), learning system(readily available knowledge for automobile tyro enthusiasts) with increased accuracy of matching. Video surveillance is a security solution for government buildings, facilities and operations. Installing this system can enhance existing security systems or help start a comprehensive security solution that can keep the building, employees and records safe. The system uses a Haar cascaded classifier to detect a car in a video and implements an efficient algorithm to extract the color of it along with the confidence rating. An gadabouts trained classifier is used to detect the logo (Suzuki/Toyota/Hyunadai) of the car whose accuracy is enhanced by implementing SURF matching. A combination of blobs and contour tracing is applied for shape detection and model classification while number plate detection is performed in a smart and efficient algorithm which uses morphological operations and contour tracing. Finally, a trained, single perceptron neural network model is integrated with the system for identifying the make of the car. A thorough work on the system has proved it to be efficient and accurate, under different illumination conditions, when tested with a huge dataset which has been collected over a period of six months. © 2012 Science Publications.

Cite this Research Publication : Dr. Senthil Kumar T. and Sivanandam, S. Nb, “An improved approach for detecting car in video using neural network model”, Journal of Computer Science, vol. 8, pp. 1759-1768, 2012.

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