BACK PROPAGATION NEURAL NETWORK FOR OBJECT DETECTION AND CLASSIFICATION OVER VIDEO DATA

The vehicle detection on roads becomes an important task when it comes to the urban surveillance and monitoring the traffic on the roads. The live monitoring of the tasks over the roads requires the specifically designed algorithm for vehicular movement tracking. The vehicular movement tracking enables the vehicle detection model to track the object from the points of its appearance in the video and till its disappearance out of the video. Each vehicular object must be tracked and localized for the efficient classification. The image processing techniques in this paper include the various object detection and classification methods. The neural network has been incorporated for the vehicular detection and classification on the basis of the knowledge-driven application. The accuracy of the object algorithm determines the robustness of the vehicular classification and categorization for the in-depth analysis. The performance of the proposed model has been evaluated after conducting the appropriate series of experiments. The proposed model has been found accurate and efficient on the limited data under the experiments performed