@article {10.3844/jcssp.2012.1759.1768,
article_type = {journal},
title = {An Improved Approach for Detecting Car in Video using Neural Network Model},
author = {Kumar, T. Senthil and Sivanandam, S. N.},
volume = {8},
number = {10},
year = {2012},
month = {Sep},
pages = {1759-1768},
doi = {10.3844/jcssp.2012.1759.1768},
url = {https://thescipub.com/abstract/jcssp.2012.1759.1768},
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.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}