Publications

Abstract : Deployment of effective surveillance and security measures is important in these days. The proposed approach is able to detect, identify and track of different types of vehicles and people entering the secured premises, to avoid any mishap from happening. There are many existing approaches which are used for tracking objects. Edge matching, Divide-and-Conquer search, Gradient matching, Histograms of receptive field responses, Pose clustering, SIFT, SURF etc are some of the approaches applied. All these methods are either Appearance based methods or Feature based methods. They lag in one or the other way when it comes to real time applications.So there has been a need for creating a new system that could combine positive aspects of both the methods and increase the efficiency in tracking objects, when it comes to real life scenario. A novel approach for car detection and classification is presented, to a whole new level, by devising a system that takes the video of a vehicle as input, detects and classifies the vehicle based on its make and model. It takes into consideration four prominent features namely Logo of vehicle, its number plate, colour and shape. Logo detector and recognizer algorithms are implemented to find the manufacturer of the vehicle. The detection process is based on the Adaboost algorithm, which is a cascade of binary features to rapidly locate and detect logos. The number plate region is localized and extracted using blob extraction method. Then colour of the vehicle is retrieved by applying Haar cascade classifier to first localize on the vehicle region and then applying a novel algorithm to find colour. Shape of the vehicle is also extracted using blob extraction method. The classification is done by a very efficient algorithm called Support vector machines. Experimental results show that our system is a viable approach and achieves good feature extraction and classification rates across a range of videos with vehicles under different conditions. © EuroJournals Publishing, Inc. 2012.