Development of Adaptive Tracking using Advance Filter and Selection Features Method

Abstract

Recently, Kalman filter (KF)-based algorithms of tracking had demonstrated to be effective  However, their efficiency is limited by fixed feature selections and the possibility of model drift.  In the presented research, we offer a new adaptive feature selection-based tracking approach that maintains the KF’s excellent   discriminating power . Depending on scores of confidence regarding features in every one of frames, the suggested approach might select (automatically) either SIFT feature or the colour feature for the tracking . With a use of KF, a response map related to the SIFT features and color features are retrieved first . The color features that distinguish the luminance from the color are extracted using the Lab color space . Second, the average peak-to-correlation energy is used for the determination of the confidence region and the target's possible location  . Finally, a total of 3 criteria have been utilized in order to choose the appropriate feature for present frame in order to execute adaptive tracking . On OTB benchmark datasets, the experimental findings show that the suggested tracker performs better in comparison with other state-of-art techniques