Slashdot Mirror
Algorithms for Motion Tracking?
Keith Handy asks: "I seem to be unable to find algorithms and/or open source programs that will do accurate motion tracking, i.e. you mark a point on an object in frame 36, and the program can follow that point on that object through all the frames following it. This is useful not just for analyzing motion, but also for interpolating/extrapolating frames of video -- so if you had something at only 15 fps, you could generate inbetween frames (which are not just crossfades between the frames) and actually smooth the effect of the motion. Not something so complicated as to get into actual physics -- just something that will indicate where (in 2D only) that part of the object has moved from one frame to the next, for any given point in the whole picture. And for that matter it doesn't have to be 100% accurate, just any means of generating a reasonable motion-flow map." This doesn't strike me as an easy algorithm to develop, but are there any papers online or offline, that might describe an algorithm that can at least track objects in an image?
"In other words, I want something that does this,
in order to write code that will do things like this and this. I already know how to write code to blur and warp images, so to be able to track motion would give me (and you) the same capabilities as these expensive plug-ins.
Anyone know any other resources, directions, or existing code I could look into to find out more about how this works, so I can incorporate it into my own programming instead of paying hundreds or thousands of dollars for limited, proprietary use of the technology?"
Hmm, not sure about any resources on the net, but I did similar stuff at uni so I can recommend a book. Try Image processing, analysis and machine vision by hlavac et al. It's a very good book with plenty of code-neutral algorithms. Good luck.
J-aims
--
Yo, whatever happened to peas? Join T( H)GS
I think the best use of this would be in video compression -- if you can recognize the movement of objects between frames, you can encode how much things have moved instead of re-encoding the entire image.
Which is exactly what MPEG does... very crudely. The MPEG solution seems to be to compare a block (8x8?) of pixels with every block in the previous frame.
The fact that MPEG doesn't use anything more sophisticated than this suggests to me that there probably aren't any algorithms which consistently work better.
Tarsnap: Online backups for the truly paranoid
have a look at openCV (stands for open computer vision), it was originally developed by intel, but was later open souced. runs on both linux and windows and is mainly used for real time motion tracking of live video sources. i'm sure there are some pretty nice algorithms in the source there somewhere. They have their stuff on sourceforge
and a yahoo groups support forum thing here
the original intel pages are here
cheers,
bjpirt
Compute the 2D FFT of each frame (in grayscale), then get
the intercorrelation function of two neighbouring
frames. The maxima are more or less where
the objects have moved.
I only used this method on artificially generated
frames, ie 1 frame with translation and noise
added. Still, the intercorrelation sinks quite
fast. On natural images, there must be a lot
of fiddling to do.
Google passes Turing test : see my journal
Have a look at the KLT tracker - that will probably do what you want.
An implementation can be found here:
http://vision.stanford.edu/~birch/klt/
http://motion.technolust.cx
there are some examples and a sample video which demonstrate tracking "motion."
For other applications (e.g., colorization), you need somewhat better segmentation. Doing this well in the general case is still a research topic; but that's good: you can get lots of research software from around the net that does this sort of thing. Look for keywords like "computer vision", "motion", "segmentation", and "tracking" on Google.
why don't you try this far-fetched possibility:
:-)
break up the iimage into N x N submatrices, and do a fourier transform on each subsection of the image. then do this for the next frame, and calculate the phase differences between each frame, and use linear/cubic/etc interpolation to generate the frames in between. not too difficult, and I think there is even a 2-D FFT library located somwhere on download.com. this, however might introduce a couple of artifacts, but if you're doing high framerate video, it shouldn't be too noticeable.
or even more far-fetched:
assuming that the translation of the objects in the image plane between frames are small and uniform enough, you might also be able to pull this off with a properly trained neural network on subsections of the image (so each individual feature fits approximately in each subsection). neural networks can do non-linear regression, but thier outputs are continuous, so I figure if you train it right, it'll give you what you want.
good luck
Once you have done this for every block in the original frame, you have a set of motion vectors from which you can construct an intermediate frame.
I found the audio commentry on the SG1 dvd for Small Victories facinating, with how they used lasers as points (they later brushed out some), so they could sync the CGI bugs with the moving camera.
h tm l
Also, the BBC have something camera based in the works
http://www.bbc.co.uk/rd/tour/virtualproduction.
checking the size of a jpg file would not work. maybe an uncompressed jpg file would contain the information. A very small change in the original picture could lead to very much change in the resulting bytes of the jpg file.
but try seaching for webcam motion detector on google and you will find some useful stuff.