OpenCV学习——物体跟踪的粒子滤波算法实现之背景介绍

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这个项目是由俄亥俄州立大学（OSU）一位博士生所写，http://web.engr.oregonstate.edu/~hess/，这位博士在其个人主页上对该项目进行了如下描述：
Object tracking is a tricky problem. A general, all-purpose object tracking algorithm must deal with difficulties like camera motion, erratic object motion, cluttered backgrounds, and other moving objects. Such hurdles render general image processing techniques an inadequate solution to the object tracking problem.

Particle filtering is a Monte Carlo sampling approach to Bayesian filtering. It has many uses but has become the state of the art in object tracking. Conceptually, a particle filtering algorithm maintains a probability distribution over the state of the system it is monitoring, in this case, the state -- location, scale, etc. -- of the object being tracked. In most cases, non-linearity and non-Gaussianity in the object's motion and likelihood models yields an intractable filtering distribution. Particle filtering overcomes this intractability by representing the distribution as a set of weighted samples, or particles. Each particle represents a possible instantiation of the state of the system. In other words, each particle describes one possible location of the object being tracked. The set of particles contains more weight at locations where the object being tracked is more likely to be. We can thus determine the most probable state of the object by finding the location in the particle filtering distribution with the highest weight.
大致翻译如下：
物体追踪是一个棘手的问题。一个普适的，通用的物体追踪算法必须应对诸如摄像头运动、不稳定物体的追踪、复杂的背景、以及存在其他移动物体等困难的状况。粒子滤波算法是一个采用蒙特卡罗采样进行贝叶斯滤波的方法。这种方法有许多的用途，但它已经成为进行物体追踪最好的方法。从概念上讲，一个粒子滤波算法包含一个被监视系统的状态的概率分布。在本项目中，状态就是指被追踪物体的位置、大小等等。在许多情况下，非线性和非高斯型在物体的运动和相似性建模上会得到一个难以处理的滤波分布。粒子滤波采用将这个分布重新表示为一组加权值，或称为粒子的方法克服了这个困难。每个粒子表示一个可能的系统状态实例。换句话说，每个粒子描述了被追踪物体可能处于的一个方位。一个粒子集包含了被追踪物体最有可能处于的方位。因此，我们可以通过寻找在粒子滤波分布中最大的权重来确定物体最有可能处于的状态。