问题描述
我在人们在计算机视觉跟踪。我有观察（blob作为背景减除后blob检测的输出），我想推断已经产生这些观察的对象。
I'm on people tracking in computer vision. I have observations (blob as an output of blob detection after background subtraction) and I want to infer the objects that have produced these observations.
我困扰了一些卡尔曼滤波器码。这对我来说很清楚，但我的问题是多对象跟踪：我的问题是有时观察是不完整/嘈杂。让我更好地解释 - 在一个明确的观察测试，我有1 blob为每个人。卡尔曼滤波器可以帮助我将人的噪声路径平滑为平滑的曲线。但是，这不是我的问题;问题是，有时blob检测不完美，我有2个blob的1人（例如，如果我想跟踪的人正在穿一件T恤的背景颜色相同）或有时我有1 blob为2人们（例如，如果2个人拥抱自己或彼此太靠近）。
I have troubled with some Kalman filter code. And it's quite clear to me, but my problem is multi-object tracking: my problem is that sometimes the observations are incomplete/noisy. Let me explain better - In a test with clear observations, I have 1 blob for each person. Kalman filter can help me in smoothing the noisy path of the person into a smoothed curve. But, this is not my problem; The problem is that sometimes blob detection is not perfect and I have 2 blobs for 1 person (for example if the person I want to track is dressing a t-shirt of the same color of the background) or sometimes I have 1 blob for 2 persons (for example if the 2 persons are hugging themselves or are too near each other).
我搜索了一些理论，我发现很多论文正在解决使用粒子滤波器的对象跟踪的问题。所以我研究了贝叶斯过滤器，蒙特卡罗方法，重要性抽样，这是一个有点清楚（我没有概率的数学知识理解一切，但想法是清楚的）。
I have searched some theory and I have found a lot of papers that are solving the problem of object tracking with particle filter. So I studied Bayesian filter, Monte Carlo method, importance sampling and it is a little bit clear (I don't have math knowledge on probability to understand everything but the idea is clear).
无论如何，我仍然不明白粒子滤波器如何帮助我检测2个blob对应于1个对象或1个blob对应于2个对象的情况。
Anyway, I don't still understand how particle filter can help me in detecting cases where 2 blobs correspond to 1 object or 1 blob correspond to 2 objects.
推荐答案
卡尔曼滤波器在这种情况下是一种背景减法法。它不能处理数据关联，只能处理高斯噪声。
Kalman Filter are a background subtractor approach in this case. It can not handle data association and only gaussian noise.
最后，我重新实现了由对象检测激活的基于直方图的粒子滤波器。
In the end I have re-implemented the histogram based particle filter activated by object detections.
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If anyone is interested in that just ask as a comment!
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