Classification and indexing in highway traffic videos

This paper involves object detection, object tracking and classification of objects, and indexing from vehicle surveillance videos automatically. The objects are detected using optical flow method and tracked using Kalman Filtering method. The objects extracted are classified using ten features including shape based features such as area, height, width, compactness factor, elongation factor, skewness, perimeter, orientation, aspect ratio and extent. A comparative analysis is presented in this paper for the classification of objects (car, truck, auto, human, motorcycle, none) based on Multi-class SVM (one vs. all), Back-propagation, and Adaptive Hierarchical Multi-class SVM (Support Vector Machine). The results obtained from the above methods have an accuracy of 92 percent for Multi-SVM (one vs. all), 87.8 percent for Adaptive Hierarchical Multi-class SVM, and 82 percent for back-propagation. Using the trained classifier obtained using Multi-class SVM (one vs. all), the objects are classified and counted in realtime. In addition, objects are automatically indexed using type of object, size, color and the portion of the video it appears in.