Vision based data extraction of vehicles in traffic
Dipankar Mudoi, Parismita A. Kashyap · 2014
With the rise in traffic related crimes the need for an efficient automated surveillance system has become of utmost importance. This paper proposes a system to monitor video from traffic cameras and process it in real time for storing essential information of the vehicles in traffic. Histogram of Oriented Gradients (HOG) of extracted frames is used as features for classification (vehicle frame and non vehicle frame). The classifier is designed based on Support Vector Machine (SVM). The subtracted image acquired from a dynamically updated background image is used to extract the vehicle image for recognition using trained Artificial Neural Network(ANN). The system is designed to store details like vehicle make, model, color and time of passing the camera in a database (Microsoft Access (MS Access)). Finally the stored details are made available through a Graphical User Interface(GUI) designed using Visual Basic(VB) that will provide an user with the options of selecting a time window to look for the vehicles that have passed within that interval or to enter a car model to check if it has passed that point at any time. The system is modeled in MATLAB and tested in a real time environment in one of the busiest road in Kamrup district of Assam and provides satisfactory performance.