Human Activity Analysis: Iterative Weak/Self-Supervised Learning Frameworks for Detecting Abnormal Events
This repository contains the code for the IJCB'20 paper "Human Activity Analysis: Iterative Weak/Self-Supervised Learning Frameworks for Detecting Abnormal Events"
pip install -r requirements.txt # use flag --user if permission needed- Keras: 2.3.1
- TensorFlow: 2.2.0
- Scipy: 1.4.1
- Scikit-learn: 0.23.1
- NumPy: 1.19.0
In order to employ the WS/SS framework and apply self-supervision to your dataset, normalization is required since we are working with coupled deep learning networks working at different levels. Follow the steps inside the utils folder to successfully normalize your dataset.
The UBI-Fights Dataset provides a wide diversity in fighting scenarios with 1000 videos, where 784 are normal daily life situations and 216 contain a fighting event. This dataset is fully-annotated at the frame-level.
To train your model and employ self-supervision for each network, the following directory structure is created:
|-- **annotation
| |-- strong // Insert here your .csv files to be used by the SS Model
| | |-- train.csv // empty, to be filled by the WS Model and Bayesian Classifier
| | |-- test.csv
| | |-- val.csv
| | |-- unlabeled_set.csv // Same remaining videos unlabeled from the weak, but in C3D raw segment format
| | |-- test_notes.csv
| | `-- val_notes.csv
| `-- weak // Insert here your .csv files to be used by the WS Model
| |-- train.csv // Small percentage (i.e., 30%) of the original training set
| |-- test.csv
| |-- val.csv
| |-- unlabeled_set.csv // Remaining videos are unlabeled for self-supervision purposes
| |-- test_notes.csv
| `-- val_notes.csv
|-- models
| |-- pattern_model
| | |-- 0 // WSS framework iterations
| | |-- 1
| | |-- ...
| |-- strong_model
| | |-- 0 // WSS framework iterations
| | |-- 1
| | |-- ...
| `-- weak_model
| |-- 0 // WSS framework iterations
| |-- 1
| |-- ...
`-- results
|-- pattern
| `-- VAL // Directory for validation stats
|-- strong
| |-- FINAL // Directory for test stats
| `-- VAL
`-- weak
|-- FINAL
`-- VAL
- Training: Start framework training.
python3 ws_ss.py --save_best_weak --save_best_strong # Set flag settings accordingly to the ws_ss.py file- Restart from checkpoint: To analyse the unlabeled scores, after a network executed over the unlabeled set, select the previous iteration and network.
python3 ws_ss.py --start_iteration 0 --weak_free_checkpoint --save_best_weak --save_best_strong # Set flag settings accordingly to the ws_ss.py file- Testing: To evaluate the WS or SS model in the specific iteration over the testing data or validation data.
python3 test.py --strong_model --model_iteration 0 --path_test annotation/strong/test.csv --path_test_note annotation/strong/test_notes.csv # Example to evaluate the SS model at iteration 0 of the WS/SS framework in the testing set- Inference: Visualization of the response scores from a video. Install C3D first and download their C3D model.
python3 inference.py --root_video abs/path/video --root_frames path/save/frames --root_C3D_dir abs/path/C3D --root_features abs/path/save/features # Example to obtain a video visualizing the response scores of your modelPlease cite this paper in your publications if it helps your research:
@inproceedings{degardin2020human,
title={Human Activity Analysis: Iterative Weak/Self-Supervised Learning Frameworks for Detecting Abnormal Events},
author={Degardin, Bruno and Proen{\c{c}}a, Hugo},
booktitle={2020 IEEE International Joint Conference on Biometrics (IJCB)},
pages={1--7},
organization={IEEE}
}
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