多重Blob跟踪 [英] Multiple Blob Tracking
问题描述
我想追踪影片中的某些路况。我发现了一个很好的方法来解决这里的问题：链接
I would like to track some traffic signs from a video. I found a nice way to solve the problem here: Link
我现在的问题是：我应该如何处理新的传入blob的情况？我的意思是一个Blob可以定义一个搜索区域，但也许在下一个框架中还有第二个出现？我应该如何处理这个？
My question now is: How should I handle the case of new incoming blobs? I mean for a Blob one could define a search-region, but maybe in the next frame there is also a second thing that appears? How should I handle this?
推荐答案
从我提供的论文中我明白，这个系统已经做了跟踪几个迹象一次，出现和消失。看到§2的结尾：
from what I understand from the paper you provide, this system is already made to track several signs at a time, appearing and disappearing. See the end of §2 :
最新跟踪的blob存储在临时存储器中。帧（t + 1）中的斑点与临时存储器（...）中的斑点匹配，因此，当符号在特定帧中消失时，它可以在再次出现时在下一帧中被跟踪。
the latest tracked blobs are stored in a temporary memory. Blobs in frame (t+1) are matched with those in the temporary memory (...) thus, when a sign disappears in particular frames, it could be tracked in the next frame when it appears again.
下一节（3 - blob匹配）解释了如何识别您正在从一个帧跟踪到另一个帧的符号。但是如果你可以匹配它们（识别它们），它也意味着你也可以不认识他们，意味着有新的迹象：它们必须被添加到内存。
The next § (3 - blob matching) explains how you "recognize" the signs you are tracking from one frame to another. But if you can match them (recognize them), it also means that you can also not recognize them, meaning that there are new signs : They must then be added to the memory.
我认为（但我可以错了）误导你的是搜索区域缩减。我认为对于每个符号/ blob，这个区域减少是独立（见§2，边界框 确定）。
I think (but I can be wrong) that what is misleading you is the "search region reduction". I think that this region reduction is done independently for every sign/blob (see §2, the "bounding boxes are determined"). So it doesn't matter how many signs there are.
那么算法如下：
- 对于每个帧：
- for each frame :
- 使用卡尔曼滤波器检测blobs对于每个blob：
- 使用第3节中描述的环分区方法将此blob与已知blob匹配
- if blob不匹配，将其添加到内存作为新的blob
- detect "blobs" (= traffic sign candidates) using the Kalman-Filters
- for each blob :
- match this blob with the already known blobs using the ring partitioned method described in §3
- if the blob doesn't match, add it to the memory as a new blob
本文不介绍何时从最新已知blob内存中删除blob。由于算法被设计为工作，即使一个斑点丢失几帧，然后重新出现（例如由卡车或电杆隐藏）和任何运动（所以我们不能推断符号将消失到侧面图片或更大），我认为（我的意见），我们可以使用时间限制和区域碰撞检测。如果一个新的blob出现在我们期望已知的blob但不匹配的区域，则意味着旧的blob不再相关。
The article doesn't cover when to remove a blob from the "latest known blobs" memory. Since the algorithm is made to work even if a blob is missing for a few frames then reappear (hidden by a truck or an electric pole for example) and whatever the movement (so we can't infer that signs will disappear to the sides of the picture or after getting bigger), I think (my opinion) that we could use both a time limit and an "area collision" detection. If a new blob appears in an area where we would expect a known blob but doesn't match it, then it means that the old blob is no longer relevant.
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