opencv中的快速运动和物体检测
我们如何才能同时检测快速运动和物体，让我举个例子，...... 假设有一个足球比赛视频，我想以最大的准确度检测每个球员的位置。我正在考虑人体检测，但如果我们看到足球比赛视频，那么人体检测就没有任何意义，因为我们可以将人体视为对象也许我们可以通过斑点检测来做到这一点，但是斑点存在很多问题，例如：-
1）我想将每个玩家分开。因此，如果玩家发生碰撞，那么斑点检测将无济于事。所以单独识别玩家会出现问题 2）第二个是体育场灯光问题。
那么是否有任何特定的算法或方法或库可以做到这一点..？ 我看过一些研究论文，但不满意......所以建议任何与此相关的内容，例如任何文章、算法、库、任何方法、任何研究论文等，并请大家在此表达您的观点。
How can we detect rapid motion and object simultaneously, let me give an example,....
suppose there is one soccer match video, and i want to detect position of each and every players with maximum accuracy.i was thinking about human detection but if we see soccer match video then there is nothing with human detection because we can consider human as objects.may be we can do this with blob detection but there are many problems with blobs like:-
1) I want to separate each and every player. so if players will collide then blob detection will not help. so there will problem to identify player separately
2) second will be problem of lights on stadium.
so is there any particular algorithm or method or library to do this..?
i've seen some research paper but not satisfied...so suggest anything related to this like any article,algorithm,library,any method, any research paper etc. and please all express your views in this.
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为了快速可靠的人体检测，Dalal 和 Triggs 的梯度直方图< /a> 被普遍认为非常好。你试过玩那个吗？
既然您提到了快速的运动变化，您是否担心快速的摄像机运动或快速的球员/球运动？
您可以进行 2D 或 3D 视频稳定来修复摄像机运动（尝试出色的 VirtualDub Deshaker 插件）。
对于快速的玩家运动，背景扣除或其他斑点检测肯定会有所帮助。您可以使用它来获得粗略的运动学估计，并将其用作模糊内核的估计。然后，这可以用于对包含播放器的图像芯片进行去模糊。
您可以进行额外的处理，以根据 OCRing 球衣号码等建立身份。
您提到了对体育场灯光的担忧。主要问题是它会投射阴影吗？ HOG 探测器可以解决这个问题。用于获取模糊内核的斑点检测应该仍然可以很好地处理阴影。
如果您可以控制相机，您可能需要减少曝光时间以减少模糊。去噪技术可用于减少极弱光下出现的 CCD 噪声，而密集光流方法可对齐帧，并通过添加去噪帧将信号恢复到合理的水平。
For fast and reliable human detection, Dalal and Triggs' Histogram of Gradients is generally accepted as very good. Have you tried playing with that?
Since you mentioned rapid motion changes, are you worried about fast camera motion or fast player/ball motion?
You can do 2D or 3D video stabilization to fix camera motion (try the excellent Deshaker plugin for VirtualDub).
For fast player motion, background subtraction or other blob detection will definitely help. You can use that to get a rough kinematic estimate and use that as an estimate of your blur kernel. This can then be used to deblur the image chip containing the player.
You can do additional processing to establish identify based upon OCRing jersey numbers, etc.
You mentioned concern about lights on the stadium. Is the main issue that it will cast shadows? That can be dealt with by the HOG detector. Blob detection to get blur kernel should still work fine with the shadow.
If you have control over the camera, you may want to reduce exposure times to reduce blur. Denoising techniques can be used to reduce CCD noise that occurs with extreme low light and dense optical flow approaches align the frames and boost the signal back up to something reasonable via adding the denoised frames.