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DiffCam – demos related to motion detection with JavaScript

55 points by dedalus 10y ago

olympus 10y ago
Excellent demos. One suggestion for the creator/maintainer of the demo pages: It would be nice if the imageDiff demo had two pre-made images to do a diff on. I spent a few minutes taking a shot with my webcam and then another with my had in the scene just to see how the static diff worked. Pre-loading a shot of a background and one with something added to the image would be a nice start to the demo instead of making the user drag/drop their own images. As a totally unrelated note, if this interests you, you might also get a kick out of coherent change detection (CCD) which works on synthetic aperture radar (SAR) images: http://defense-update.com/features/du-2-05/sensor-5.htm http://defense-update.com/features/du-2-05/sensor-5.htm. CCD has been used by military intelligence for a while now, and although a lot of the cool stuff is classified, the basics of the technique are unclassified and kind of cool.
- lonekorean 10y ago
  Good point! I've added some sample images to the demo to make it easier to play with. Thanks for the feedback.
yellowbkpk 10y ago
I spent Memorial Day weekend working on Python/OpenCV traffic counter that uses similar motion detection techniques. I tried to explain how it works in the readme on the GitHub repository: https://github.com/iandees/speedtrack https://github.com/iandees/speedtrack
deleted 10y ago
[deleted]
deckar01 10y ago
I have been working on a binary image diffing algorithm for a project called Narcis (https://github.com/deckar01/narcis https://github.com/deckar01/narcis). The idea is to provide visual regression testing for integration tests. It's not a new concept, but the weakest part of the solutions I have seen are really ugly diffs. I am also playing around with the idea of streamlining a manual review UX instead of the standard change threshold concept. I have mainly focused on assumptions that can be made about screenshots of a webpage which allows more fine-tuned diffing behavior. Demos: https://narcis.us/test/google/master/home/000000/diff/000001 https://narcis.us/test/google/master/home/000000/diff/000001 https://narcis.us/test/google/master/home/000000/diff/000006 https://narcis.us/test/google/master/home/000000/diff/000006 https://narcis.us/test/google/master/home/000000/diff/000009 https://narcis.us/test/google/master/home/000000/diff/000009
beardicus 10y ago
For anybody doing similar things in javascript, you might look into jsfeat: https://inspirit.github.io/jsfeat https://inspirit.github.io/jsfeat It provides some basic building blocks for interesting computer vision in js.

55 points by dedalus 10y ago

Excellent demos. One suggestion for the creator/maintainer of the demo pages: It would be nice if the imageDiff demo had two pre-made images to do a diff on. I spent a few minutes taking a shot with my webcam and then another with my had in the scene just to see how the static diff worked. Pre-loading a shot of a background and one with something added to the image would be a nice start to the demo instead of making the user drag/drop their own images. As a totally unrelated note, if this interests you, you might also get a kick out of coherent change detection (CCD) which works on synthetic aperture radar (SAR) images: http://defense-update.com/features/du-2-05/sensor-5.htm http://defense-update.com/features/du-2-05/sensor-5.htm. CCD has been used by military intelligence for a while now, and although a lot of the cool stuff is classified, the basics of the technique are unclassified and kind of cool.

lonekorean 10y ago
Good point! I've added some sample images to the demo to make it easier to play with. Thanks for the feedback.

yellowbkpk 10y ago

I spent Memorial Day weekend working on Python/OpenCV traffic counter that uses similar motion detection techniques. I tried to explain how it works in the readme on the GitHub repository: https://github.com/iandees/speedtrack https://github.com/iandees/speedtrack

deleted 10y ago

[deleted]

deckar01 10y ago

I have been working on a binary image diffing algorithm for a project called Narcis (https://github.com/deckar01/narcis https://github.com/deckar01/narcis). The idea is to provide visual regression testing for integration tests. It's not a new concept, but the weakest part of the solutions I have seen are really ugly diffs. I am also playing around with the idea of streamlining a manual review UX instead of the standard change threshold concept. I have mainly focused on assumptions that can be made about screenshots of a webpage which allows more fine-tuned diffing behavior. Demos: https://narcis.us/test/google/master/home/000000/diff/000001 https://narcis.us/test/google/master/home/000000/diff/000001 https://narcis.us/test/google/master/home/000000/diff/000006 https://narcis.us/test/google/master/home/000000/diff/000006 https://narcis.us/test/google/master/home/000000/diff/000009 https://narcis.us/test/google/master/home/000000/diff/000009

beardicus 10y ago

For anybody doing similar things in javascript, you might look into jsfeat: https://inspirit.github.io/jsfeat https://inspirit.github.io/jsfeat It provides some basic building blocks for interesting computer vision in js.