Robust Automatic Monocular Vehicle Speed Estimation for Traffic Surveillance

This repository contains the implementation of the following paper:

@inproceedings{icctv,
  author    = {Jerome Revaud and Martin Humenberger},
  title     = {Robust Automatic Monocular Vehicle Speed Estimation for Traffic Surveillance},
  booktitle = {ICCV},
  year      = {2021},
}

License

Our code is released under the Creative Commons BY-NC-SA 3.0 (see LICENSE for more details), available only for non-commercial use.

Requirements

You need

• Python 3.8+ equipped with standard scientific packages and PyTorch / TorchVision:

  tqdm >= 4
  PIL >= 8.1.1
  numpy >= 1.19
  scipy >= 1.6.1
  torch >= 1.8.0
  torchvision >= 0.9.0
  cv2 >= 4.5.1
  filterpy >= 1.4.5
  

• An object tracker. In the ICCV paper, we used the SORT tracker, but any object tracker of your preference can do.

Reproducing results on the BrnoCompSpeed dataset

Note: Since we cannot share the 3D car models from the Unity library due to license issues, this code only reproduces results from the learned method given a model pretrained based on these 3D car models.

1. Download BrnoCompSpeed dataset and evaluation code as explained in JakubSochor's github.

2. Extract car tracks.

   python run_brnocompspeed.py extract_tracks --dataset-dir /path/to/brnocompspeed --num-frames 5000

   Here we are limiting the extraction to the first 5000 frames. It will save car detections (boxes) in /path/to/brnocompspeed/detections and tracks in /path/to/brnocompspeed/tracks.

3. Compute homographies given a pretrained model.

   python run_brnocompspeed.py compute_homographies --dataset-dir /path/to/brnocompspeed --num-frames 5000 --model models/trained_transformer.pt

   This will write all homographies in /path/to/brnocompspeed/homography/.

   Note: Since this process involves randomness due to RANSAC, you may obtain a slighlty different results compared to what is published in the paper. Therefore we provide the homographies used in the paper in data/brno_homographies.zip.

4. Optionally, you can have a quick evaluation of the homographies.

   python run_brnocompspeed.py evaluate_homographies --dataset-dir /path/to/brnocompspeed

   Note: these results are computed based on ground-truth boxes, hence the output results does not reflect the actual accuracy of the full system (i.e. detector, tracker and speed estimator jointly). Rather, it provides a quick estimates of how good are the estimated homographies based on ground-truth tracks.

5. Evaluate results using BrnoCompSpeed's evaluation code.

   # re-compute tracks, this time for entire videos (it will take a while)
   python run_brnocompspeed.py extract_tracks --dataset-dir /path/to/brnocompspeed 
   # export homographies and tracks in json format
   python run_brnocompspeed.py export_json --dataset-dir /path/to/brnocompspeed

   Then run the evaluation code (caution: it's written in python2).

   • First, set RUN_FOR_SYSTEMS = ["transformer"] in /path/to/brnocompspeed/code/config.py.
   • Then, execute the evaluation code: cd /path/to/brnocompspeed/code && python eval.py -rc

CCTV dataset

We are currently working to release the CCTV dataset proposed in the paper. In the meantime, please reach out to us if you need this dataset.