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Autor: TobiasWeis
Lane detection

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 😉
lanedet_00004871

Training Cascades to detect cars

I spent some time on training several cascades to detect cars in ego-view automotive videos,
and will now document what I’ve learned.

I will use the existing OpenCV-tools.

Data preparation
./cascadetraining/
-> pos/ 1000 images containing the desired object
-> pos.info (containing the filenames of the objects, number of objects in the frame and bounding boxes in the format x,y,width,height)
-> neg/ 2000 images that do not contain cars at all
-> negs.txt text-file containing the filenames of all negative images

For the positive images I used tight bounding boxes. You actually do not need as many negative images as you want to use negative samples later on, as the training-script will sample patches from the negative images given, so it can actually be less images than negative samples.

Examples:
Some of the positive images, the bounding-boxes have been annotated by hand (ground-truth-data):

#1444377217419041_00002412

#1444377329818721_00003832


#1444377488943090_00005818

#1444377515982041_00006142


BCCN 2015 Poster

We presented our poster at the BCCN conference 2015 in Heidelberg. It describes our system platform and a first case study of brakelight detection.

[pdf height=“950px“]http://blog.tobias-weis.de/wp-content/uploads/2015/09/poster-sys.pdf[/pdf]