An Improved Approach for Detecting Car in Video using Neural Network Model
T. Senthil Kumar and S. N. Sivanandam
DOI : 10.3844/jcssp.2012.1759.1768
Journal of Computer Science
Volume 8, Issue 10
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 T. Senthil Kumar and S. N. Sivanandam. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.