Invariant feature matching based adaptive bandwidth mean shift and its application to infrared object tracking

Mean shift algorithm has grained great success in object tracking domain due to its ease of implementation, real time response and robust tracking performance, however, the fixed kernel bandwidth may cause tracking failure for size changing objects. A novel object tracking algorithm for FLIR imagery is proposed based on mean shift with adaptive bandwidth. The scale invariant feature transform is employed to compute the affine model between the successive frames. Then, the scale and orientation of the kernel can be estimated by the gained parameters. Experiment results verify the effectives and robustness of this extraction algorithm which can improve the tracking performance efficiently.