Optimization and Tracking of Vehicle Stable Features Using Vision Sensor in Outdoor Scenario

features for robust tracking for augmented reality with change in scene. The technique is based on nearestneighbor search, but this method requires high computation time in the training phase of initialization. To overcome the limitation of the recent tracking algorithms, this paper proposed an optimized vehicle tracking approach to track the stable vehicle features efficiently in less time with the use of vision sensor. The proposed method tracks the stable and neuro optimized matched features in the segmented objects of the traffic sequence. In this paper, a feature-based tracking scheme for fast vehicle detection is proposed. In the proposed scheme, all the moving vehicles are tracked with the fast feature matching between the different segmented vehicles, and the system performs the efficient vehicle tracking in outdoor environment by using segmented stable vehicle features which are optimized by the neural network methodology. This paper is organized as follows: Section II present the related work for the vehicle tracking and feature based methods. Section III discusses the proposed method scheme and the experiments for tracking the moving vehicles are detailed. In Section IV, the tracking results are shown and the outcomes of the proposed method is discussed which shows advantages over the recent tracking techniques in terms of computation times. Section V concludes this paper.