使用openCV进行自然特征跟踪-评估选项 [英] Natural feature tracking with openCV- evaluating the options
问题描述
简而言之，使用OpenCv在网络摄像头供稿中实现对特定图像(照片/图片/徽标)的跟踪有哪些可用选项?特别是我正在尝试整理以下方面的意见:
In brief, what are the available options for implementing the Tracking of a particular Image(A photo/graphic/logo) in webcam feed using OpenCv?In particular i am trying to collate opinion about the following:
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HaarTraining是否会过大(考虑这不是3d对象，而只是要跟踪的图像)还是唯一的出路?
Would HaarTraining be overkill(considering that it is not 3d objects but simply Images to be tracked) or is it the only way out?
已经尝试了模板匹配，基于颜色的检测，但这些方法根本无法在变化的照明/比例/方向下提供可靠的跟踪.
Have tried Template Matching, Color-based detection but these don't offer reliable tracking under varying illumination/Scale/Orientation at all.
我是OpenCV的相对新手，我以前对SO的查询就证明了这一点(非常有用的答复).是否有任何线索或链接可能是开始使用OpenCV实施NFT的良好资源?
Am a relative beginner to OpenCV , as is evident by my previous queries on SO (very helpful replies). Any cues or links to what could be good resources for beginning NFT implementation with OpenCV?
推荐答案
您能否谈谈您的要求?即，您期望什么类型的外观变化/您对环境有多大的控制权.在速度/功率/资源占用方面，您有什么类型的约束条件?
Can you talk a bit more about your requirements? Namely, what type of appearance variations do you expect/how much control you have over the environment. What type of constraints do you have in terms of speed/power/resource footprint?
没有这些，我只能对您正在谈论的3条路径进行一些总体评估.
Without those, I can only give some general assessment to the 3 paths you are talking about.
1. Haar可以很好且快速地工作，尤其是在识别方面.
1. Haar would work well and fast, particularly for instance recognition.
请注意，除非您使用全面的模板进行培训以涵盖各种观点，否则Haar对于3D而言并不是很好. Haar级联的后代应用是Viola Jones的面部检测系统，该系统主要面向正面(当然可以接受许多其他方面的训练)
Note that Haar doesn't work all that well for 3D unless you train with a full spectrum of templates to cover various perspectives. The poster child application of Haar cascades is Viola Jones' face detection system which is largely geared towards frontal faces (can certainly be trained for many other things)
有关使用OpenCV进行Haar培训的教程，请参见此处.
For a tutorial on doing Haar training using OpenCV, see here.
2. 尝试使用NCC或更好的方法，为模板使用Lucas Kanade跟踪(cvCalcOpticalFlowPyrLK是金字塔形的，就像从粗到细的LK中一样-4级金字塔通常效果很好).通常可以在不更改模板的情况下达到10％的比例或10度的旋转度.除此之外，您还可以拥有会随着时间推移而变化的自动演变的模板.
2. Try NCC or better yet, Lucas Kanade tracking (cvCalcOpticalFlowPyrLK which is a pyramidal as in coarse-to-fine LK - a 4 level pyramid usually works well) for a template. Usually good upto 10% scale or 10 degrees rotation without template changes. Beyond that, you can have automatically evolving templates which can drift over time.
有关快速光流/跟踪教程，请参见此
For a quick Optical Flow/tracking tutorial, see this.
3. SIFT/SURF确实可以很好地工作.我建议采取一些额外的几何验证步骤，以消除虚假匹配.
3. SIFT/SURF would indeed work very well. I'd suggest some additional geometric verification step to remove spurious matches.
我会有点担心所涉及的计算时间.如果照明/比例尺/平面内旋转不明显，则SIFT可能会过大.如果您确实需要它，请查看Changchang Wu出色的SIFTGPU 示例.注意:第三方，不是OpenCV.
I'd be a bit concerned about the amount of computational time involved. If there isn't significant illumination/scale/in-plane rotation, then SIFT is probably overkill. If you truly need it, check out Changchang Wu's excellent SIFTGPU implmentation. Note: 3rd party, not OpenCV.
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