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This repository is intended to host the diagnosis tool for analyzing temporal action localization algorithms. This tool is first presented as part of our DETAD paper.
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Diagnosing Error in Temporal Action Detectors (DETAD)

This repository is intended to host the diagnosis tool for analyzing temporal action localization algorithms. It includes three analyses: false positive analysis, localization metric sensitivity analysis, and false negative analysis. Please take a look at our DETAD paper, in which we first proposed this tool and presented an in-depth diagnosis of the state-of-the-art temporal action localization algorithms on both ActivityNet v1.3 and THUMOS14.

If you find any piece of code valuable for your research, please cite this work:

  title={Diagnosing Error in Temporal Action Detectors},
  author={Alwassel, Humam and Caba Heilbron, Fabian and Escorcia, Victor and Ghanem, Bernard},
  booktitle={The European Conference on Computer Vision (ECCV)},

How to run it?

Install conda, create the environment from the environment.yml file, and then activate the environment:

conda env create -f environment.yml
source activate detad

Run the false positive analysis (the other two analyses are run in a similar way):

cd src
python --ground_truth_filename ../data/activity_net_train_val_extra_characteristics.v1-3.min.json --subset validation --prediction_filename ../data/anet18_winner_validation.json --output_folder ../sample_output/validation

The code will produce PFD files with the analysis results in the --output_folder specified. Take a look at the sample_output folder for sample analysis results for a sample method on the validation and testing ActivityNet v1.3 dataset. We thank Tianwei Lin (the 2018 ActivityNet Challenge winner of the Temporal Action Localization Task) for providing us with a sample submission file on the ActivityNet dataset.

What datasets can you use?

We provide augmented dataset files for the ActivityNet v1.3 (validation) and THUMOS14 dataset in the data folder. However, this tool is developed for any general dataset and any augmentation characteristics.

Who is behind it?

Contributor Contributor Contributor Advisor
Humam Alwassel Fabian Caba Victor Escorcia Bernard Ghanem

Do you want to contribute?

  1. Check the open issues or open a new issue to start a discussion around your new idea or the bug you found
  2. Fork the repository and make your changes
  3. Send a pull request
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