Detection and Classification of Moving Objects by Using Real Time Traffic Flux through Differential and Graphical Analysis

This paper is focussed on detection and classification of moving objects using differential and graphical techniques. The basic idea used is variation in the traffic flux density due to presence of objects in the scene. Accurate Traffic flux estimation will play vital role in object detection and classification. Dynamic selection of images from the sequence and detection and classification of objects implemented successfully in order to reduce the computation time. The designed technique are evaluated with 15 different video sequences and weighed thoroughly with simple confidence measures. In the present work we have achieved real time analysis with normal video rate. And for object classification computation we are taking specific frame gap which saves computational time. The result produced with this analysis is extremely good and beneficial in real time traffic control, detecting and classifying objects in urban areas. In the normal condition the average accuracy raised near to 97%.