关于使用卡尔曼滤波器跟踪运动球的问题 [英] Question on tracking a moving ball using Kalman filter
问题描述
我想学习使用卡尔曼滤波器跟踪移动的球.虽然有很多教程，但我还有一些问题.
I want to learn to track a moving ball using Kalman filter. Although many tutorial are available, I still have some questions.
如果我们能在视频序列的每一帧中提取出球，我们就可以知道球的位置.那么，为什么我们还需要使用卡尔曼滤波器呢?卡尔曼滤波器在这里的作用是什么?
If we can extract the ball in each frame of the video sequence, we will know the position of the ball. Then, why do we need to use Kalman fiter anymore? What is the job of Kalman filter here?
卡尔曼滤波器:x(k+1) = A.x(k) + B.u(k) + 噪声
Kalman filter: x(k+1) = A.x(k) + B.u(k) + noise
y(k) = C.x(k) + noise
那么，我们如何定义 A、B、C 呢?假设我们要跟踪移动的球?
Then, how do we define A, B, C ? suppose we want to track the moving ball?
3 .如果我们知道先前状态 x(k-1) 和当前状态 y(k) 的测量值，我们可以计算估计状态.追踪移动球时的测量"是什么?
3 . If we know the previous state x(k-1) and the measurement of the current state y(k), we can compute the estimated state. What is the "measurement" in case of tracking the moving ball?
推荐答案
基本上你使用跟踪方法来跟随你的场景中的球.您仍然需要在每个时间步中检测球.
well basically you use tracking method to follow the ball in your scene. You'd still have to detect the ball in each timestep.
(1)
假设您到达一个点，另一个球进入场景.所以你需要找到一种方法来识别每个球.让情况变得更糟，甚至让他们相互交叉或在路上设置障碍物，这可能会使球隐藏一段时间.
Assume you come to a point where another ball comes into the scene. So you need to find a method to identify each ball. Make it worse and even let them cross each other or have obstacles in the way which might hide the ball for a little while.
然后，您的跟踪方法(即卡尔曼滤波器)将根据球之前的移动方式继续在估计的轨迹上移动.
Your tracking method (i.e. Kalman filter) will then continue its movement on the estimated trajectory, based on how the ball has moved before.
因此，如果您只想知道球在每一帧中的位置，则不需要跟踪方法，但如果您想识别并跟踪一个球，则需要一个跟踪器，即卡尔曼滤波器或多- 对象跟踪我更喜欢粒子过滤器 (http://en.wikipedia.org/wiki/Particle_Filter).
So if you just want to know where the ball is in each frame you don't need a tracking method, but if yo uwant to identify and follow one ball you'd need a tracker, i.e. a Kalman filter or for multi-object tracking I'd prefer a Particle Filter (http://en.wikipedia.org/wiki/Particle_Filter).
(2)
现在无法帮助您，因为我现在还不太了解卡尔曼滤波器.但基本上 A 应该是从步骤 x(t-1) 到 x(t) 的转移矩阵，B 应该是参考模型.但正如我所说，如果你可能得到多个球，你可能想看看粒子过滤器.
Can't help you here right now since I am not too far into the Kalman Filter right now. But basically A should be the transition matrix from step x(t-1) to x(t) and B should be the reference model. But as I've said if its likely you get more than one ball you might wanna have a look at the particle filter.
(3)
测量值将是您在图像上测量的位置.基本上是你的球的中点.您将使用此测量值来校正估计的路径.所以基本上你将球在当前时间步(取决于它之前的运动)应该在的位置与它的实际位置进行比较.
The measurement would be the position you measured on you image. Basically the midpoint of your ball. You'd use this measurement to correct the estimated path. So basically you compare the position the ball should be at at current timestep (depending on it's former movement) with it's actual position.
希望对您有所帮助...如果没有，请继续询问...
I hope that helps... if not, keep asking...
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