计算人体跟踪中的色散 [英] Calculating dispersion in Human Tracking

问题描述

我目前正在尝试从中央电视台追踪人头。我目前使用颜色直方图和LBP直方图比较来检查边界框之间的亲和力。

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.

我在阅读以下链接中的一篇论文：纸张，其中描述了分散度量。然而我还是不能清楚地得到它。例如，我不能理解pi，j在方程中是指什么。有人可以和

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?

您可以帮助我们：）

推荐答案

本文使用背景模型解决跟踪问题，因为大多数CCTV跟踪方法。 BG模型产生前景掩模，并且前述p_ij在一些形态之后涉及该掩模。具体地，它们基于FG掩模孔中允许的间隙的阈值，尝试将前景斑点分离成分量。此过程的最终结果是一组二进制掩码，每个假设对象一个。然后，这些掩模用于使用空间和时间一致性的跟踪。在我看来，这是一种处理视频序列的老式方式，只有在处理能力有限且场景不拥挤时才有意义。

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.

回答您的问题问题，如果O是与假设对象之一相关的掩码，则p_ij是掩码内的（i，j）位置中的二进制像素。因此，c_x和c_y是二进制形状的质心，并且色散只是从形状的质心的平均距离（对于较大的物体来说它是较大的），这在跟踪中强制了比例一致性，但是在非常如果你有一个校准过的相机，你可以做得更好。

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.

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