Tracking objects using colorspaces
The information obtained by frame differencing is useful, but we will not be able to build a robust tracker with it. It is very sensitive to noise and it does not really track an object completely. To build a robust object tracker, we need to know what characteristics of the object can be used to track it accurately. This is where color spaces become relevant.
An image can be represented using various color spaces. The RGB color space is probably the most popular color space, but it does not lend itself nicely to applications like object tracking. So we will be using the HSV color space instead. It is an intuitive color space model that is closer to how humans perceive color. You can learn more about it here: http://infohost.nmt.edu/tcc/help/pubs/colortheory/web/hsv.html . We can convert the captured frame from RGB to HSV colorspace, and then use color thresholding to track any given object. We should note that we need to know the color...
