请在本教程中解释卡尔曼过滤器的用途 [英] Please explain what is the use of kalman filter in this tutorial

问题描述

一个人发布了本教程关于使用卡尔曼滤波器进行对象跟踪.许多人都对星级很高的评价，所以这不是一个错误/错误的教程.

A guy posted this tutorial about object tracking using Kalman filter. Many people rated high star so it is not a fault/wrong tutorial.

但是，有人发布了以下问题:" 在这段代码中，您已经在每一帧中进行了检测，并且此输出作为卡尔曼滤镜的输入提供，因此背景减法和卡尔曼滤镜将给出相似的结果，因此请您在此处说明卡尔曼滤镜的用法. "

However, a guys posted the following question:" In this code you have done detection in every frame and this output is provided as the input to the kalman filter.So background subtraction and kalman filter will give similar results.So please can you explain the use of kalman filter here. "

我对他也有同样的想法. 有人可以在这里解释卡尔曼滤波器的用法吗?

I have the same thought with him. Can anybody explain the use of Kalman filter here?

推荐答案

使用背景减法的简单检测将在每个采样周期内给出结果，但是结果将很嘈杂(由于测量噪声和量化)以及检测误差将会产生巨大的影响.

A simple detection with background subtraction will give a result in every sample period, however the result will be noisy (due to measurement noise and perhaps quantization) and detection errors will have a huge impact.

如果您想观察一个物体，通常会对它的运动方式有所了解.它不会从一个位置跳到下一个位置，而是以连续的方式移动到该位置.卡尔曼滤波器结合了简单检测算法中的测量结果，并将它们与您拥有的关于物体的模型知识(位置不能跳跃)结合在一起，因此它过滤了测量结果并考虑了测量历史.考虑到线性系统，可以证明考虑到系统的测量噪声，卡尔曼滤波器是对数据进行滤波的最佳方法.

If you want to observe an object you usually know something about how it will move. It won't jump from one position to the next but move there in a continuous way. The Kalman filter combines the measurements from the simple detection algorithm and combines them with the model knowledge that you have about the object (position can't jump), so it filters the measurement and considers the history of the measurements. Considering a linear system, you can prove that the Kalman filter is the optimal way of filtering the data considering the measurement noise of the system.

在本教程中，显然，下一步将使用卡尔曼滤波器来预测球的位置.在向下运动中，这效果很好.由于过滤器对地板一无所知，因此当球撞击地面时，预测当然是错误的.在向上运动期间，预测仍然会遭受此误差的影响.

In this tutorial, the Kalman filter is obviously used to predict the position of the ball in the next step. In the downwards motion, this works pretty well. As the filter doesn't know anything about the floor, the prediction is of course wrong when the ball hits the ground. During the upwards motion, the prediction still suffers from this error.

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