Assume a stationary scene with the camera moving around. A typical example would be the view from a window in a train Journey. If you notice properly closer objects move faster compared to distant objects . This can be used as a basic property to estimate depth for the case of videos. Optical Flow can be used to estimate velocity at each point in a video and this velocity can be used for a relative depth estimation. OpenCV gives a very good implementation of Optical flow and hence we can obtain a fairly accurate depth map using optical flow.
A very appropriate explanation of Optical Flow using Lucas-Kanade Method is explained here. While Lucas-Kanade Method is useful for finding optical flow for a sparse set of points, we are actually interested in calculating Optical FLow vectors for the entire image, i.e for every pixels. There exists a lot of good implementations of dense optical Flow in OpenCV.
Even though I the title of this blog was on depth estimation using Optical Flow, I was unable to obtain any datasets with a moving camera and a stationary background. I would be updating this post with the depth map created with the help of Optical flow on a later time.
A very appropriate explanation of Optical Flow using Lucas-Kanade Method is explained here. While Lucas-Kanade Method is useful for finding optical flow for a sparse set of points, we are actually interested in calculating Optical FLow vectors for the entire image, i.e for every pixels. There exists a lot of good implementations of dense optical Flow in OpenCV.
Even though I the title of this blog was on depth estimation using Optical Flow, I was unable to obtain any datasets with a moving camera and a stationary background. I would be updating this post with the depth map created with the help of Optical flow on a later time.
