In this chapter, we explored a way to label the potentially interesting objects in a visual scene, even if their shape and number is unknown. We explored natural image statistics using Fourier analysis, and implemented a state-of-the-art method for extracting the visually salient regions in the natural scenes. Furthermore, we combined the output of the salience detector with a tracking algorithm to track multiple objects of unknown shape and number in a video sequence of a soccer game.
It would now be possible to extend our algorithm to feature more complicated feature descriptions of proto-objects. In fact, mean-shift tracking might fail when the objects rapidly change size, as would be the case if an object of interest were to come straight at the camera. A more powerful tracker, which comes for free in OpenCV, is cv2.CamShift. CAMShift stands for Continuously Adaptive Mean-Shift, and bestows upon mean-shift the power to adaptively change the window size. Of course, it would also...
