问题
I am currently trying to track human heads from a CCTV. I am currently using colour histogram and LBP histogram comparison to check the affinity between bounding boxes. However sometimes these are not enough.
I was reading through a paper in the following link : paper where dispersion metric is described. However I still cannot clearly get it. For example I cannot understand what pi,j is referring to in the equation. Can someone kindly & clearly explain how I can find dispersion between bounding boxes in separate frames please?
You assistance is much appreciated :)
回答1:
This paper tackles the tracking problem using a background model, as most CCTV tracking methods do. The BG model produces a foreground mask, and the aforementioned p_ij relates to this mask after some morphology. Specifically, they try to separate foreground blobs into components, based on thresholds on allowed 'gaps' in FG mask holes. The end result of this procedure is a set of binary masks, one for each hypothesized object. These masks are then used for tracking using spatial and temporal consistency. In my opinion, this is an old fashioned way of processing video sequences, only relevant if you're limited in processing power and the scenes are not crowded.
To answer your question, if O is the mask related to one of the hypothesized objects, then p_ij is the binary pixel in the (i,j) location within the mask. Thus, c_x and c_y are the center of mass of the binary shape, and the dispersion is simply the average distance from the center of mass for the shape (it is larger for larger objects. This enforces scale consistency in tracking, but in a very weak manner. You can do much better if you have a calibrated camera.
来源：https://stackoverflow.com/questions/29052905/calculating-dispersion-in-human-tracking