如何从视频中分类真阴性？ [英] How to classify true negative from a video?

问题描述

为了性能测量的目的，我试图绘制ROC曲线。在ROC曲线中，我必须在x轴绘制假阳性率（FPR），在y轴绘制真实阳性率（TPR）。我们知道，

For a performance measuring purpose I am trying to draw ROC curve. In ROC curve I have to plot False Positive Rate (FPR) in x-axis and True Positive Rate (TPR) in y-axis. As we know,

FPR = FP /（FP + TN）

FPR = FP/(FP+TN)

因此，在下面的图片中如何检测True Negative（TN）？我使用HOG分类器来检测人。我标记了矩形1,2,3,4,5,6（或应该是7），以显示应该忽略的人类对象，而不是分类为人类。我认为这些都是True Negative。

So in the following picture how can i detect True Negative(TN) ? I have used HOG classifier to detect human. I marked with rectangle 1,2,3,4,5,6(or should be 7) to show the human objects that should be ignored and not to classify as human. and I think those are True Negative.

在这张图片中我想说我的假设，我们知道，

In this picture i want to say my assumption,as we know,

假阴性：结果应该是正的， 。

False negative: Result should have been positive, but is negative.

假阳性：结果应为负值，但为正值。

False positive: Result should have been negative, but is positive.

真阴性：结果应为负值且为负值

True negative: Result should have been negative and is negative

所以我认为在这个帧FP = 0，TP = 0，FN = 0但不知道TN，是6还是7或任何其他？如果我错了，请更正我的FP，TP和FN。我看到了此问题如何在滑动窗口中对True Negatives进行分类对象检测？这是非常有帮助的，但是我仍然需要为这个场景计算FPR。

So i think in this frame FP = 0, TP = 0, FN = 0 but not sure about TN, is it 6 or 7 or anything other? Please correct me also about FP, TP, and FN if i am wrong. I saw this question How to categorize True Negatives in sliding window object detection? which was really helpful but still i have to calculate FPR for this scenario.

推荐答案

这些值从这样的图像，你需要更多的数据（知识实际发生了什么）。但是你需要的只是总量这些窗口，这是一个常数N.现在，似乎所有这些窗口都错了（没有人在人类），因此：

You cannot calculate these values from such image, you need more data (knowledge what is actually happening). But what you need is probably just total amount of these windows, which is some constant N. Now, it seems like all these windows are wrong (none is on the human), thus:

• FP = 6（你的方法声称有6个人，但没有一个声明是有效的，因为它们完全关闭 - 但是如果这只是可视化问题，并且方法实际上捕获了有效的人，这6个应该移动到TP）


• TP = 0（它不正确地标记任何人）


• FN = 10（如果我计数正确，在这张图片上有10个人，所有这些都丢失了）


• TN = N - 16，其中N是所有分析窗口的数目，因为所有这些都被正确地分类为缺少人类，最多达到10个FN和6个FP，总计达到这16个。

• FP = 6 (your method claims there are 6 people, but none of these claims is valid since they are completely off - however if this is just visualization issue, and method actually captured valid people, this 6 should be moved to TP instead)
• TP = 0 (it does not correctly mark any human)
• FN = 10 (if I counted correctly there are 10 people at this image, and all these are missing)
• TN = N - 16, where N is number of all analized windows, since all of them are correctly classified as "lack of human" up to 10 FNs and 6 FPs, which add up to these 16.

一般来说

• FP =有多少实际不是人类标记为人


• TP =有多少实际人类标记为人类


• FN =有多少人 > （未标记为人）


• TN = 标记为人）

• FP = how many actual not humans are marked "human"
• TP = how many actual humans are marked "human"
• FN = how many actual humans are correctly ignored (not marked "human")
• TN = how many actual not humans are correctly ignored (not marked "human")

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