jareducherek / speed_detection

Speed Detection Repository

Peronal project inspired by the Comma AI speed challenge: github.com/commaai/speedchallenge

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Your goal is to predict the speed of a car from a video.

• data/train.mp4 is a video of driving containing 20400 frames. Video is shot at 20 fps.
• data/train.txt contains the speed of the car at each frame, one speed on each line.
• data/test.mp4 is a different driving video containing 10798 frames. Video is shot at 20 fps.

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Installation instructions:

• If necessary, pip3 install --upgrade pip
• clone this repo
• download data from the linked repo
• make create_environment
• conda activate speed_detection
• make requirements (adjust Makefile if not Cuda 10.2)

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Please run nbs/data_processing to set up data, which also runs a segmentation network over all data to be used for image masking. Finalized notebooks: fixed_point_regression, SuperPoint_fixed_point_regression.

Our approach ultimately uses SuperPoint for feature point tracking, and normalizes the found vectors that appear only on the road in front of the car. A regression model is fit to the median of these tracks for each image pair. No classical image processing algorithms are needed such as optical flow or corner detectors.