Using imagecolorat() for motion detection

I've got a security camera set up, and i'm comparing 1 minute batches of images to check if there's been any motion. I have 10 coordinates that I check between each image. If any pixels don't match the previous image, it triggers a warning message.

Problem is, it works too well.

The logic is basically if imagecolorat() is greater or less than a 10% difference from the previous imagecolorat(), it triggers. So, if a cloud comes over the house, it triggers. Basically any change in light triggers it. So, I've moved the threshold from 10% to 30% and it triggers less but I'm worried now that if I move any past that, real motion wont be de开发者_如何学Pythontected.

Note: I'm using the raw output of imagecolorat(), not the RGB values. I'm not sure if this would have an impact.

You are looking for larger discontinuities - things like noise and slow variation should be discounted (unless this motion detection is for very slow moving small things, like garden gnomes).

Therefore, do a histogram equalize or something similar before image subtraction to account for global shifts in light, and do some filtering / edge enhancement before differencing to enhance the changes. Using more of the image will be better I would think, than just 10 points.

Histogram equalization entails looping through the image, and counting bins for each brightness value - so you have a resulting data set that says how many pixels are in a set of tonal ranges. In other words, say you divvy up into 16 bins - pixels that have a greyscale value (or alternately, the Brightness in an HSB model) in the value of 0..15 (assuming an 8 bit image here in this channel) are all in bin 1. Then you go forth and compute a series of linear stretches to apply to each bin so that it occupies an output range in proportion to its population. So if ALL of your pixels are in the 0.. 15 bin, you would just multiply everything by 16 to stretch them out. The goal is that your histogram of your equalized image is flat - equal amounts of pixel in every bin.

Edge enhancement can be simply done with the application of a Sobel filter.