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"# CNN_Dashcam_Speed_Detector" | ||
# CNN Dashcam Speed Detector | ||
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This project was inspired by comma.ai's Speed Challenge: given their training video (onboard dashcam video of 20400 frames @20fps) with the respective ground truth speeds, the objective is to train a model that will be able to predict the car's speed in another test video. | ||
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## Dataset | ||
The dataset is composed by: | ||
* Training video (20400 frames @ 20fps) with respective ground truth speeds | ||
* Test video (10798 frames @ 20fps) | ||
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The training video features Highway driving in the first part, and Town driving in the second (with intersections, stop signals, ...). Also, illumination conditions change a lot during the video, going through tunnels and under bridges. | ||
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Speed values are changing a lot too, being high and homogeneous during Highway sections, and slower and intermittent during city sections. All of this makes the training video very heterogeneous in its features. | ||
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The test video also shows both city and highway driving. |