This chapter explored the vast and complex topic of video analysis and tracking objects.

We learned about video background subtraction with a basic motion detection technique that calculates frame differences, and then moved to more complex and efficient tools such as BackgroundSubtractor.

We then explored two very important video analysis algorithms: Meanshift and CAMShift. In the course of this, we talked in detail about color histograms and back projections. We also familiarized ourselves with the Kalman filter, and its usefulness in a computer vision context. Finally, we put all our knowledge together in a sample surveillance application, which tracks moving objects in a video.

Now that our foundation in OpenCV and machine learning is solidifying, we are ready to tackle artificial neural networks and dive deeper into artificial intelligence with OpenCV and Python in the next chapter.