I am currently researching the use of a low resolution camera facing vertically at the ground (fixed height) to measure the speed (speed of the camera passing over the surface). Using OpenCV 2.1 with C++.
Since the entire background will be constantly moving, translating and/or rotating between consequtive frames, what would be the most suitable method in determining the displacement of the frames in a ‘useable value’ form? (Function that returns frame displacement?) Then based on the height of the camera and the frame area captured (dimensions of the frame in real world), I would be able to calculate the displacement in the real world based on the frame displacement, then calculating the speed for a measured time interval.
Trying to determine my method of approach or if any example code is available, converting a frame displacement (or displacement of a set of pixels) into a distance displacement based on the height of the camera.
Thanks,
Josh.
It depends on your knowledge in computer vision. For the start, I would use what opencv can offer. please have a look at the feature2d module.
What you need is to first extract feature points (e.g. sift or surf), then use its build in matching algorithms to match points extracted from two frames. Each match will give you some constraints, and you will end up solving a over-saturated Ax=B.
Of course, do your experiments offline, i.e. shooting a video first and then operate on the single images.
UPDATE:
In case of mulit-camera calibration, your goal is to determine the 3D location of each camera, which is exactly what you have. Imagine instead of moving your single camera around, you have as many cameras as the number of images in the video captured by your single camera and you want to know the 3D location of each camera location, which represent the location of each image being taken by your single moving camera.
There is a matrix where you can map any 3D point in the world to a 2D point on your image see wiki. The camera matrix consists of 2 parts, intrinsic and extrinsic parameters. I (maybe inexactly) referred intrinsic parameter as the internal matrix. The intrinsic parameters consists of static parameters for a single camera (e.g. focal length), while the extrinsic ones consists of the location and rotation of your camera.
Now, once you have the intrinsic parameters of your camera and the matched points, you can then stack a lot of those projection equations on top of each other and solve the system for both the actual 3D location of all your matched points and all the extrinsic parameters.