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How can we detect rapid motion and object simultaneously, let me give an example,.... suppose there is one soccer match video, and i want to detect position of each and every players with maximum accuracy.i was thinking about human detection but if we see soccer match video then there is nothing with human detection because we can consider human as objects.may be we can do this with blob detection but there are many problems with blobs like:-

1) I want to separate each and every player. so if players will collide then blob detection will not help. so there will problem to identify player separately 2) second will be problem of lights on stadium.

so is there any particular algorithm or method or library to do this..? i've seen some research paper but not satisfied...so suggest anything related to this like any article,algorithm,library,any method, any research paper etc. and please all express your views in this.

Lyndon Peters
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For fast and reliable human detection, Dalal and Triggs' Histogram of Gradients is generally accepted as very good. Have you tried playing with that?

Since you mentioned rapid motion changes, are you worried about fast camera motion or fast player/ball motion?

You can do 2D or 3D video stabilization to fix camera motion (try the excellent Deshaker plugin for VirtualDub).

For fast player motion, background subtraction or other blob detection will definitely help. You can use that to get a rough kinematic estimate and use that as an estimate of your blur kernel. This can then be used to deblur the image chip containing the player.

You can do additional processing to establish identify based upon OCRing jersey numbers, etc.

You mentioned concern about lights on the stadium. Is the main issue that it will cast shadows? That can be dealt with by the HOG detector. Blob detection to get blur kernel should still work fine with the shadow.

If you have control over the camera, you may want to reduce exposure times to reduce blur. Denoising techniques can be used to reduce CCD noise that occurs with extreme low light and dense optical flow approaches align the frames and boost the signal back up to something reasonable via adding the denoised frames.

peakxu
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