init - 初始化项目
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Lee Nony
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doc/tutorials/others/optical_flow.markdown
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doc/tutorials/others/optical_flow.markdown
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Optical Flow {#tutorial_optical_flow}
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============
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@tableofcontents
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@prev_tutorial{tutorial_meanshift}
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@next_tutorial{tutorial_cascade_classifier}
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Goal
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----
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In this chapter,
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- We will understand the concepts of optical flow and its estimation using Lucas-Kanade
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method.
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- We will use functions like **cv.calcOpticalFlowPyrLK()** to track feature points in a
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video.
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- We will create a dense optical flow field using the **cv.calcOpticalFlowFarneback()** method.
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Optical Flow
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------------
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Optical flow is the pattern of apparent motion of image objects between two consecutive frames
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caused by the movement of object or camera. It is 2D vector field where each vector is a
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displacement vector showing the movement of points from first frame to second. Consider the image
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below (Image Courtesy: [Wikipedia article on Optical Flow](http://en.wikipedia.org/wiki/Optical_flow)).
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It shows a ball moving in 5 consecutive frames. The arrow shows its displacement vector. Optical
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flow has many applications in areas like :
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- Structure from Motion
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- Video Compression
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- Video Stabilization ...
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Optical flow works on several assumptions:
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-# The pixel intensities of an object do not change between consecutive frames.
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2. Neighbouring pixels have similar motion.
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Consider a pixel \f$I(x,y,t)\f$ in first frame (Check a new dimension, time, is added here. Earlier we
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were working with images only, so no need of time). It moves by distance \f$(dx,dy)\f$ in next frame
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taken after \f$dt\f$ time. So since those pixels are the same and intensity does not change, we can say,
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\f[I(x,y,t) = I(x+dx, y+dy, t+dt)\f]
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Then take taylor series approximation of right-hand side, remove common terms and divide by \f$dt\f$ to
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get the following equation:
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\f[f_x u + f_y v + f_t = 0 \;\f]
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where:
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\f[f_x = \frac{\partial f}{\partial x} \; ; \; f_y = \frac{\partial f}{\partial y}\f]\f[u = \frac{dx}{dt} \; ; \; v = \frac{dy}{dt}\f]
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Above equation is called Optical Flow equation. In it, we can find \f$f_x\f$ and \f$f_y\f$, they are image
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gradients. Similarly \f$f_t\f$ is the gradient along time. But \f$(u,v)\f$ is unknown. We cannot solve this
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one equation with two unknown variables. So several methods are provided to solve this problem and
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one of them is Lucas-Kanade.
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### Lucas-Kanade method
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We have seen an assumption before, that all the neighbouring pixels will have similar motion.
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Lucas-Kanade method takes a 3x3 patch around the point. So all the 9 points have the same motion. We
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can find \f$(f_x, f_y, f_t)\f$ for these 9 points. So now our problem becomes solving 9 equations with
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two unknown variables which is over-determined. A better solution is obtained with least square fit
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method. Below is the final solution which is two equation-two unknown problem and solve to get the
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solution.
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\f[\begin{bmatrix} u \\ v \end{bmatrix} =
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\begin{bmatrix}
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\sum_{i}{f_{x_i}}^2 & \sum_{i}{f_{x_i} f_{y_i} } \\
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\sum_{i}{f_{x_i} f_{y_i}} & \sum_{i}{f_{y_i}}^2
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\end{bmatrix}^{-1}
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\begin{bmatrix}
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- \sum_{i}{f_{x_i} f_{t_i}} \\
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- \sum_{i}{f_{y_i} f_{t_i}}
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\end{bmatrix}\f]
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( Check similarity of inverse matrix with Harris corner detector. It denotes that corners are better
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points to be tracked.)
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So from the user point of view, the idea is simple, we give some points to track, we receive the optical
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flow vectors of those points. But again there are some problems. Until now, we were dealing with
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small motions, so it fails when there is a large motion. To deal with this we use pyramids. When we go up in
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the pyramid, small motions are removed and large motions become small motions. So by applying
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Lucas-Kanade there, we get optical flow along with the scale.
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Lucas-Kanade Optical Flow in OpenCV
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-----------------------------------
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OpenCV provides all these in a single function, **cv.calcOpticalFlowPyrLK()**. Here, we create a
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simple application which tracks some points in a video. To decide the points, we use
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**cv.goodFeaturesToTrack()**. We take the first frame, detect some Shi-Tomasi corner points in it,
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then we iteratively track those points using Lucas-Kanade optical flow. For the function
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**cv.calcOpticalFlowPyrLK()** we pass the previous frame, previous points and next frame. It
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returns next points along with some status numbers which has a value of 1 if next point is found,
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else zero. We iteratively pass these next points as previous points in next step. See the code
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below:
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@add_toggle_cpp
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- **Downloadable code**: Click
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[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/video/optical_flow/optical_flow.cpp)
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- **Code at glance:**
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@include samples/cpp/tutorial_code/video/optical_flow/optical_flow.cpp
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@end_toggle
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@add_toggle_python
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- **Downloadable code**: Click
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[here](https://github.com/opencv/opencv/tree/master/samples/python/tutorial_code/video/optical_flow/optical_flow.py)
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- **Code at glance:**
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@include samples/python/tutorial_code/video/optical_flow/optical_flow.py
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@end_toggle
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@add_toggle_java
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- **Downloadable code**: Click
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[here](https://github.com/opencv/opencv/tree/master/samples/java/tutorial_code/video/optical_flow/OpticalFlowDemo.java)
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- **Code at glance:**
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@include samples/java/tutorial_code/video/optical_flow/OpticalFlowDemo.java
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@end_toggle
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(This code doesn't check how correct are the next keypoints. So even if any feature point disappears
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in image, there is a chance that optical flow finds the next point which may look close to it. So
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actually for a robust tracking, corner points should be detected in particular intervals. OpenCV
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samples comes up with such a sample which finds the feature points at every 5 frames. It also run a
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backward-check of the optical flow points got to select only good ones. Check
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samples/python/lk_track.py).
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See the results we got:
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Dense Optical Flow in OpenCV
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----------------------------
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Lucas-Kanade method computes optical flow for a sparse feature set (in our example, corners detected
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using Shi-Tomasi algorithm). OpenCV provides another algorithm to find the dense optical flow. It
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computes the optical flow for all the points in the frame. It is based on Gunner Farneback's
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algorithm which is explained in "Two-Frame Motion Estimation Based on Polynomial Expansion" by
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Gunner Farneback in 2003.
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Below sample shows how to find the dense optical flow using above algorithm. We get a 2-channel
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array with optical flow vectors, \f$(u,v)\f$. We find their magnitude and direction. We color code the
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result for better visualization. Direction corresponds to Hue value of the image. Magnitude
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corresponds to Value plane. See the code below:
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@add_toggle_cpp
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- **Downloadable code**: Click
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[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/video/optical_flow/optical_flow_dense.cpp)
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- **Code at glance:**
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@include samples/cpp/tutorial_code/video/optical_flow/optical_flow_dense.cpp
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@end_toggle
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@add_toggle_python
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- **Downloadable code**: Click
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[here](https://github.com/opencv/opencv/tree/master/samples/python/tutorial_code/video/optical_flow/optical_flow_dense.py)
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- **Code at glance:**
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@include samples/python/tutorial_code/video/optical_flow/optical_flow_dense.py
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@end_toggle
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@add_toggle_java
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- **Downloadable code**: Click
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[here](https://github.com/opencv/opencv/tree/master/samples/java/tutorial_code/video/optical_flow/OpticalFlowDenseDemo.java)
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- **Code at glance:**
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@include samples/java/tutorial_code/video/optical_flow/OpticalFlowDenseDemo.java
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@end_toggle
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See the result below:
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