I want to use my phone as a CCTV system to control a room in my house, and display an alert when something is moving.

For now, what I have succeeded in doing is to grab a picture every minute and upload them via a PHP script to my server. Now, I would like to compare the current picture and the picture 1 minute ago and detect if someone entered the room. So basically, I would need to compare differences of pixels on the picture (but also taking in account that a cloud may just say hello and changed brightness during one minute)

Does anyone have a clue on how to achieve that or some doc to read?

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    $\begingroup$ I did something like this years ago. My technique was break the image into sections, say a 20 * 20 grid, find a value for the average color in each cell (by taking the average colour in each pixel in that cell) and storing it. Do the same for the next image and if there is enough (I'll leave that tolerance up to you) difference in the average colours, you can assume movement. Don't make the tolerance so fine that it'll notice subtle changes in light or passing shadows etc. $\endgroup$ – James Webster Nov 2 '11 at 11:20
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    $\begingroup$ Here's an interesting post on the subject that you may find useful codeproject.com/KB/audio-video/Motion_Detection.aspx $\endgroup$ – Rog Nov 2 '11 at 11:55
  • $\begingroup$ CHDK also breaks the image up into a grid. chdk.wikia.com/wiki/UBASIC/Scripts:_AdaptiveMD For detecting movement and not detecting lighting changes, I would say that a change in lots of grid cells is not movement, while a change in only a few grid cells is. $\endgroup$ – endolith Nov 3 '11 at 14:07

It seems to me what you are looking for is background subtraction technique. With noisy images and changing lighting conditions it could be nontrivial. The current state of the art technique for this is the low-rank matrix representation, but it require not two but many (~dozen) images. Some heavy duty math follow: Each image considered as a vector of pixels, vectors combined into matrix and this matrix is decomposed into low-rank matrix and remnant. Low-rank matrix columns are backgrounds and remnant is moving objects. There are some open-sourced implementations, but only for factorization itself, not complete image pipeline IIRC

Here is a paper and code for matrix factorization http://www.ece.rice.edu/~aew2/sparcs.html

Here is overview from CS blog and link to other code:



Survey of some other techniques: http://www.vis.uni-stuttgart.de/uploads/tx_vispublications/Brutzer2011-2.pdf

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    $\begingroup$ did you intentionally make this CW? $\endgroup$ – Lorem Ipsum Nov 16 '11 at 13:57
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    $\begingroup$ What does "CW" mean? $\endgroup$ – mirror2image Nov 17 '11 at 9:11
  • $\begingroup$ Ahh, wiki-question. I thought it would be good idea to introduce this quite new techto ppls doing practical things. Feel free to remove it if u disagree. Also other ppls may have more experience with this tech- I only started to go into it. $\endgroup$ – mirror2image Nov 17 '11 at 9:18
  • $\begingroup$ A CW is a community-Wiki question. What this means is that you won't get reputation (in this case 4x10=40) for the upvotes on your answer. Some people intentionally make their answers like this, but more often than not, it's accidental. I've reverted this for you, so that you get your rep. It's now 181 from 126 :) $\endgroup$ – Lorem Ipsum Nov 17 '11 at 13:40

Remember: There are shadows (not just brightness).

I don't really like the idea from James Webster, since it is basically scaling down the resolution in each dimension and comparing the scaled down images (also I don't like thresholds - they are to arbitrary, you have to test and tweak them until you get a good value and the next day these values could be obsolete because of different weather or some other environmental effects)

But to be fair, I also don't have a really good solution. My first idea was to diff each image to a reference image of the empty room and run an edge detection on the diff image. But this would also detect shadows. But I guess you can't differ between shadows and other objects...at least I don't know how. But maybe you can compare the result after the Edge detection between 2 frames, since shadows are mostly moving slowly (still there will be problems when cars move by or when a cloud moves by)

  • $\begingroup$ You want to detect something that changes suddenly. Shadows won't change much from one minute to the next. Overall brightness of the sun will, though. $\endgroup$ – endolith Nov 3 '11 at 13:59
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    $\begingroup$ Shadows will move fast, if there is e.g. a car moving by and either dropping it's own shadow through the window or dropping shadows of some other objects by its headlight. $\endgroup$ – Philipp Wendt Nov 3 '11 at 15:15

The "matrix factorization" technique will NOT help you do your job! The paper referred by @mirror2image is about the background subtraction but NOT based on "matrix factorization".

Using running video to detect moving objects (be it human or vehicles) is an active area of research.

As a basic principle the system estimates a typical static background by sampling over multiple pictures and take a difference of energy between incoming image to the background. If the energy is significant the pixel is classified as foreground. Such set of foreground tells you if there is an entry of the object in the system.

The best reference to your research paper (and also relatively simpler if you want to really implement) would be - W4 System find it here and see Picardi paper here as a more detailed survey for other techniques in the system.

There are many challenges that applies to the problem:

  1. Presence of noise creates the issues of major ambiguity. The approach here is to apply efficient temporal filtering and considering variance of noise to make it immune to threshold.

  2. Presence of shadow creates ambiguity of neither being a foreground nor. There are papers who model the color vs. intensity distinction to distinguish shadow vs. real foreground.

  3. The background can be complex like waving trees or sea etc.

  4. The background can have slow or sudden variation of lighting where earlier "learned" background is then adapted to the new one.

One of the most referred landmark paper is called Wall flower algorithm shows the best way to combine various such scenarios to produce robust moving object detection.


I don't know exact solution, but you should make some kind of hash of the image; a smaller data set extracted from the image, which is comparable better than the whole image.

I think, color histogram is a good choice for it.

If you split your image to areas and make separate histograms for these areas, you can determine the position/path of the intruder.

  • $\begingroup$ Thank a lot, I will wait for other solutions, if I cannot find better, will accept yours. FYI, I don't want to determine the path of the intruder, as 1 minute is not enough for that, but just sending an alert is great. Thanks. $\endgroup$ – Profete162 Nov 2 '11 at 11:32
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    $\begingroup$ Be careful, some intruders could finish in 1 minute! Make just as frequent check just as possible. If your program is too slow, decrease image resolution. $\endgroup$ – ern0 Nov 2 '11 at 11:36
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    $\begingroup$ Yeah, in fact, I e plan to take a picture every 10s, and only upload them when I detect an intruder, or every minut when no issue. $\endgroup$ – Profete162 Nov 2 '11 at 11:39

Taking difference twice i.e. difference of differences may help.. so if the double derivative of pixels is larger than a threshold in some regions, so you may term it as someone entered the room. Change in brightness will give approximately constant gradient throughout the image, but taking the Hessian or double derivative will give a good indication of movement or major changes in the room.


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