An Approach to Identify Automatic Vehicle System for Aerial Surveillance

In this paper we proposed an Automatic Vehicle detection system for aerial surveillance. In this system, a pixel wise classification approach for vehicle detection is proposed. From the surviving framework of vehicle detection in aerial surveillance, classifications based on region and sliding window are escaped. Since the main disadvantage is that a vehicle tends to be divided as different regions when using various colors, furthermore all the vehicles are might be grouped as single region if they are similar. To come out of this problem, a Dynamic Bayesian Network (DBN) for vehicle detection in aerial surveillance is implemented, which is based on the pixel-wise classification approach. This pixel-wise classification preserved with the characteristic mining process. The features are involved with vehicle color and local the features. Subsequently implementing these features a Dynamic Bayesian Network (DBN) is built for the classification purpose, it transforms the regional local features into quantitative detections. This experiment is accompanied with several aerial videos and the developed technique is challenging the issue with aerial surveillance images taken at various heights under the divergent angle of camera.