Wouldnt the histogram comparison fail when the hand is rotated? Also, wouldnt this mean that it is possible that if in one frame if the tracker fails, and instead thinks something else is the object, we can move to accidentally tracking another similar object (eg. the other hand)?
I would suppose a similar failure case would be when the object in question does not have the same color, for example, a table tennis bat is black one side and red on another side? Are similar colors across frames a crucial assumption of the algo?
mpotoole
That's a good question. You're absolutely right; if there's a dramatic change in visual appearance from one frame to the next (e.g., the table tennis bat switching colors from red to black), then your tracker can run into problems. If you capture a smooth video of the hand in motion (such that changes across frames are not as dramatic), then the tracking should become more reliable.
Note that we only covered one flavor of the mean-shift object tracking algorithm in this class. Instead of using a normalized color histogram as a descriptor, we could work with a descriptor that possibly less sensitive to changes in color across frames.
Wouldnt the histogram comparison fail when the hand is rotated? Also, wouldnt this mean that it is possible that if in one frame if the tracker fails, and instead thinks something else is the object, we can move to accidentally tracking another similar object (eg. the other hand)?
I would suppose a similar failure case would be when the object in question does not have the same color, for example, a table tennis bat is black one side and red on another side? Are similar colors across frames a crucial assumption of the algo?
That's a good question. You're absolutely right; if there's a dramatic change in visual appearance from one frame to the next (e.g., the table tennis bat switching colors from red to black), then your tracker can run into problems. If you capture a smooth video of the hand in motion (such that changes across frames are not as dramatic), then the tracking should become more reliable.
Note that we only covered one flavor of the mean-shift object tracking algorithm in this class. Instead of using a normalized color histogram as a descriptor, we could work with a descriptor that possibly less sensitive to changes in color across frames.