Video Image Processing: Algorithms for Motion Detection

AI Thread Summary
Computer algorithms can indeed determine if a person is moving their right arm or feet and whether they are running, primarily through image recognition techniques. Cameras capture pixel data, which algorithms analyze to identify patterns, such as edges, using methods like Fourier transforms to create unique "fingerprints" of individuals. This allows computers to distinguish between different figures, such as humans and dogs, based on these patterns.However, differentiating between specific body parts, like left and right arms, poses greater challenges, particularly in identifying the orientation of a person. Neural networks are well-suited for this task, although they are not yet perfect. The discussion highlights the complexity of training neural networks, which often requires significant resources and time compared to simpler tasks. Existing commercial applications, such as Microsoft's Xbox and Kinect, utilize these algorithms, and there are numerous resources available for those interested in the field of computer vision, including open-source libraries like OpenCV and JavaScript implementations for tasks like face detection.
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Is there a computer algorithm that can determine if a person it moving their right arm or there feet, or if they are running or not ?
 
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I guess there are programs that can determine it ( not algorithms ).

The only thing a camera/computer can see is an amount of pixels with different colors within a frame. You can make an algoritm that recognizes some patterns in the image, like an edge: Scanning the image horizontally and finding a sudden color change along a vertical line, it has found a vertical edge. In this way you can convert the image into a cartoon:
figur5.gif

There are a lot of ways to recognize a figure as a person. One way is to Fourier transform the outline of the figure, regarding the edges as a series of complex coordinates. The Fourier transform is a "finger print" of a person. Adding a tail to the right figure, the Fourier transform will change and become a finger print of a dog. The "smart" charasteristic is, that if you cancel the 1. harmonic of the right figure, you can rotate him and let him crawl uphill or downhill: He will still be recognized as a person because the angle doesn't matter when the 1. harmonic is missing.

So the idea is to sketch a lot of persons/dogs in various positions, make "finger prints" and tell the computer: this is a person and this is a dog. When the computer has such a look-up-table of finger prints and sees a person or dog, it can distinguish a dog/person by closest match.

To distisguish left/right arm is more complicated: Am I seeing the front or the back of a person?
 

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This is the type of things that neural networks will be good at. Computers are not great at this yet, there is a joke in computer science that highlights the differences between what computers and the brain are good at.

Boss: I need a program that takes the entire literary history of humanity, and maps out the use of pronouns throughout history.
Engineer: Sure, give me twenty minutes.

Boss: I need a program that tells me if this picture is of a dog.
Engineer: Sure, give me five years and a dozen research assistants.
 
newjerseyrunner said:
This is the type of things that neural networks will be good at.
Well, reducing 6 million pixels as inputs in a neural network, to say 200 harmonics will make it easier for the network to make a decision.

Often neural networks are trained by the "steepest chase" method. How will a neural network by itself find a steepest chase?
You are the one to configure groups of neurons and the network between them. Where will you start amongst 6 million pixels?
 
Yes, such algorithms exist and are present in commercial products -- such as Microsoft's XBox and Kinnect system. Note that neural networks can be used, but so are various forms of decision trees with Harr-like features and other techniques. You may want to find a general text on Computer Vision / Machine Vision as it is a very broad and active area of research and there are open source libraries like OpenCV that may interest you. There are even javascript implementations of some algorithms like face detection -- see trackingjs.com
 
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