We need to ignore some of these dark grey colors which represent a small amount of difference, and focus on the brightest white pixels which represent substantial difference. These are things we will have to pay attention to as we develop our speed measuring application.

You can also see some shadow from the turtle. What else is in the picture? You can see some white in the bottom center of the image.This is the part of the timestamp that changed from the first to the second image. The black part of the picture is pixels that were zero, the grey and white are non-zero. OK now we're on to something! you can see the outline of the turtle in its' two positions. If there is any change, the difference would something other than zero (lighter colors). If the pixels are exactly the same in each picture, they would have the same value and the difference would be zero, which is black. It would subtract the values of each pixel from its corresponding pixel on the other photo. We could have our computer compare photos inВ grey-scaleВ by subtracting one photo from the other. Each pixel has a number assigned to it and this determines what color it will be.

How can this be automated? How can you tell (or how can your computer tell) if an object is moving? One way is to compare multiple photos and see whats different. This would be pretty easy if everything were moving along a ruler, but wouldn't it be great to get the distance somehow by simply counting the number of pixels between the two positions of the objects in the pictures, or better yet having the computer do this for us. that's 0.2857 inches per second, which converts to 0.01626 miles per hour! Wow, that's actually pretty good for this type of turtle. Using the timestamps on the photos, our turtle appears to have moved 8 inches in 28 seconds.

Yawcam motion detection