Speed estimation from a single dashboard camera using Deep Convolutional Networks. This is a response to the challenge posed here: https://github.com/commaai/speedchallenge
There is an associated blog post here
See the post for more details on the experiments and the associated model architectures
| Experiment | MSE |
|---|---|
| Deep Velocity Estimation (Grayscale Input, Frame Delta: 1) | > 10 |
| Deep Velocity Estimation (RGB Input, Frame Delta: 1) | > 10 |
| Deep Convolutional Network with Farneback Flow (RGB Input) | > 10 |
| DeepER Velocity Estimation (RGB Input, Depth: 20, Frame Delta: 1) | < 1** |
** Looking for someone to independently verify the performance, if you verify, please submit an issue with your results
Processing the images requires FFMPEG. See the installation guidelines here for your platform
To install the python requirements, run:
pip install -r requirements.txt
After installing the requirements, from the root directory of the repo, run
python3 src/train.pyAs referenced above, the MSE on the model is less than 1, which could be close to SOTA. I'm looking for someone to verify the results I published on the blog.
If you do want to verify, please include the following details with your verification:
- Platform
- GPU Type
- Batch Size
- Any modifications to the parameters run
The results of the run are located here
Trained parameters for the best epoch are here
See the blog post for references to relevant papers.