Object tracking in infrared imagery

In this paper, we propose a robust approach for object tracking in infrared imagery. Our method mainly applies the image intensity histogram distribution and intensity projection distributions and computes a likelihood measure between the candidate and the model distributions by evaluating the Mean Shift Vector. In addition, Gabor filters are applied here to enhance the contrast of the object with the background, and then the scale of the track window can be selected according to the variable object size. Our method greatly improves the accuracy of object tracking and can update the model frame by frame, which means the object model does not necessarily depend on that of the first frame. The robustness of our method is supported by several different infrared imagery sequences.