Skip to content

yabufarha/anticipating-activities

master
Switch branches/tags

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
April 6, 2018 13:39
April 6, 2018 13:39
August 16, 2021 10:45
August 20, 2020 10:46
April 6, 2018 13:39
April 6, 2018 13:39

When will you do what? - Anticipating Temporal Occurrences of Activities

This repository provides a TensorFlow implementation of the paper When will you do what? - Anticipating Temporal Occurrences of Activities.

Qualitative Results:

Click on the image.

IMAGE ALT TEXT

Training:

  • download the data from https://uni-bonn.sciebo.de/s/3Wyqu3cxYSm47Kg.
  • extract it so that you have the data folder in the same directory as main.py.
  • To train the model on split1 of Breakfast dataset run python main.py --model=MODEL --action=train --vid_list_file=./data/train.split1.bundle where MODEL is cnn or rnn.
  • To change the default saving directory or the model parameters, check the list of options by running python main.py -h.

Prediction:

  • Run python main.py --model=MODEL --action=predict --vid_list_file=./data/test.split1.bundle for evaluating the the model on split1 of Breakfast.
  • To predict from ground truth observation set --input_type option to gt.
  • To check the list of options run python main.py -h.

Evaluation:

Run python eval.py --obs_perc=OBS-PERC --recog_dir=RESULTS-DIR. Where RESULTS-DIR contains the output predictions for a specific observation and prediction percentage, and OBS-PERC is the corresponding observation percentage. For example python eval.py --obs_perc=.3 --recog_dir=./save_dir/results/rnn/obs0.3-pred0.5 will evaluate the output corresponding to 0.3 observation and 0.5 prediction.

Remarks:

If you use the code, please cite

Y. Abu Farha, A. Richard, J. Gall:
When will you do what? - Anticipating Temporal Occurrences of Activities
in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018

To download the used features please visit: An end-to-end generative framework for video segmentation and recognition.

About

No description, website, or topics provided.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages