Given two image-points from two different positions of a camera, we want to calculate the 3D coordinate that this point is located at in the world. This is highly related to Structure from Motion (sfm) approaches, where we try to create a dense 3d point-cloud using consecutive images (of course this will only work if the 3D worldpoint in question is not moving itself, or if we know it’s movement):
In the video below you can see the sequence of a car driving in a city scene and braking. The layers I rendered out for groundtruth data are the rendered image with the boundingbox of the car (top left), the emission layer ( shows the brakelights when they start to emit light, top right ), the optical flow (lower left), and the depth of each pixel in the world scene ( lower right).
What is the target of all this ? Driving in an automotive scenario with a given speed and turnrate at any moment, we want to predict the displacement of a 2D-projection (pixel) between two frames:
By using the camera-calibration, I can create artificial curves and walls as 3D point-sets and project them back to 2D. Using discretized values for speed, turnrate, streetwidth and wall-height, I can then simulate the displacement of these 3D-Points when they are projected to 2D (our image).
(Note for me: this is the backprojection-code, main-file: main_displacements.py)
This will probably become one of our modalities in the future: symmetry !
Thanks to the guys at hs-niederrhein, there is symmetry-detection-code that can already be used
for some first estimates:
This software implements the gradient product transform for symmetry
detection that is described in the paper
C. Dalitz, R. Pohle-Froehlich, F. Schmitt, M. Jeltsch:
„The gradient product transform for symmetry detection
and blood vesselm extraction.“ International Conference on
Computer Vision Theory and Applications (VISAPP), pp. 177-184,
2015
And the first results look quite promising:
Today I will try to detect some lanes..
Assumptions:
– We know the lane-width (plus minus)
– We are inside the middle of a lane
– We know the camera geometry
– Based on the turnrate of the IMU we can estimate the curvature of the street
– A line in pixels can be detected by a upward flank and a downward flank
Here are some exemplary results:
1) Of course, the best one first ;)
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