I am new to matalb,I want to detect a moving human figure in live video feed, this human figure can be in different poses. Can anyone give me some advice on how to detect the human figure after detecting its(human figure) motion.
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if the camera is static, take a blank image and subtract it from the live feed to get a rough idea of what pixel values have changed. maybe division would handle different daylight conditions better. anyway, once you have something moving or different about the picture, you can do a medial transform of the shape to see how 'stick figure'-like it is. good luck
Search the File Exchange for "tracking" or see this Mathworks webinar: http://www.mathworks.com/matlabcentral/fileexchange/35646-march-2012-demo-files-for-computer-vision-with-matlab
Gait recognition is not sufficient for your purpose. Consider people on crutches, people in wheelchairs, people on unicycles, bicycles, or tricycles. Some of the robots being built in Japan apparently have quite good bipedal motion and look quite realistic, but those are not human.
Recognizing leg length accurately in order to calculate proportions in order to distinguish humans from other primates (e.g., gorillas) is difficult when one considers skirts, dresses, and long winter coats. The proportions in humans vary considerably -- consider for example dwarfism. Even without disorders such as that, if you take a male human and a female human of the same height, the female will statistically have longer legs. And I know that my proportions are at least two standard deviations from the mean.
For the purpose of this project, are you restricting "human" to Homo sapiens sapiens, or are you including Homo neanderthalensis, Homo habilis, and so on?
There are many information on tracking in there but i can not find any information on how to detect a human figure, Actually human figure can have different posses, but if i take a walking human the legs are always changing in a certain way can i take this action into consideration and deduce because of the motion part it is a human.
Employ a 2 year-old child to sit in front of the screen and "point at the person".
This solution is 100% accurate with absolutely no mathematics required. It works with occlusion, non-static background, non-static camera, image noise, poor lighting, and multiple targets.
That's how frustrating Computer Vision is!!!
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