AnomalyDetectionCVPR2018-Pytorch
Pytorch version of - https://github.com/WaqasSultani/AnomalyDetectionCVPR2018
Table of Contents
- Future Improvements
- Known Issues
- Install Anaconda Environment
- Feature Extractor Weights
- Precomputed Features
- Pre-Trained Anomaly Detector
- Features Extraction
- Training
- Generate ROC Curve
- Demo
- Cite
- FAQ
Future Improvements
In this section, I list the future improvements I intend to add to this repository. Please feel free to recommend new features. I also happily accept PR's! π
- I3D feature extraction
- MFNET feature extraction
Known Issues
- AUC is not exactly as reported in the paper (0.70 vs 0.75) - might be affected by the weights of C3D
Install Anaconda Environment
conda create --name adCVPR18 --file environment.yml -c defaults -c pytorch -c conda-forge -y
conda activate adCVPR18
Feature Extractor Weights
C3D
I couldn't upload here the weights for the C3D model because the file is too big, but it can be found here: https://github.com/DavideA/c3d-pytorch
R3D-101
https://drive.google.com/file/d/1p80RJsghFIKBSLKgtRG94LE38OGY5h4y/view?usp=share_link
R3D-152
https://drive.google.com/file/d/1irIdC_v7wa-sBpTiBlsMlS7BYNdj4Gr7/view?usp=share_link
Precomputed Features
Can be downloaded from:
C3D features
https://drive.google.com/drive/folders/1rhOuAdUqyJU4hXIhToUnh5XVvYjQiN50?usp=sharing
ResNet-101 features (by @Daniele Mascali)
https://drive.google.com/file/d/1kQAvOhtL-sGadblfd3NmDirXq8vYQPvf/view?usp=sharing
ResNet-152 features (by @Henryy-rs)
https://drive.google.com/file/d/17wdy_DS9UY37J9XTV5XCLqxOFgXiv3ZK/view
Pre-Trained Anomaly Detector
Check out exps/ for for trained models on the pre-computed features
Features Extraction
Download the dataset from: https://github.com/WaqasSultani/AnomalyDetectionCVPR2018 Arguments:
- dataset_path - path to the directory containing videos to extract features for (the dataset is available for download above)
- model_type - which type of model to use for feature extraction (necessary in order to choose the correct pre-processing)
- pretrained_3d - path to the 3D model to use for feature extraction
python feature_extractor.py --dataset_path "path-to-dataset" --model_type "fe-model-eg-c3d" --pretrained_3d "path-to-pretrained-fe"
Training
Arguments:
- features_path - path to the directory containing the extracted features (pre-computed features are available for download above, or supply your own features extracted from the previous stage)
- annotation_path - path to the annotations file (Available in this repository as
Train_annotations.txt)
python TrainingAnomalyDetector_public.py --features_path "path-to-dataset" --annotation_path "path-to-train-annos"
Generate ROC Curve
Arguments:
- features_path - path to the directory containing the extracted features (pre-computed features are available for download above, or supply your own features extracted from the previous stage)
- annotation_path - path to the annotations file (Available in this repository as
Test_annotations.txt) - model_path - path to the trained anomaly detection model
python generate_ROC.py --features_path "path-to-dataset" --annotation_path "path-to-annos" --model_path "path-to-model"
I achieve this following performance on the test-set. I'm aware that the current C3D model achieves AUC of 0.69 which is worse than the original paper. This can be caused by different weights of the C3D model or usage of a different feature extractor.
| C3D (Link) | R3D101 (Link) | R3D152 (Link) |
|---|---|---|
 |
 |
 |
Demo
Off-line (with video loader)
Arguments:
- feature_extractor - path to the 3D model to use for feature extraction
- feature_method - which type of model to use for feature extraction (necessary in order to choose the correct pre-processing)
- ad_model - path to the trained anomaly detection model
- n_segments - the number of segments to chunk the video to (the original paper uses 32 segments)
python video_demo.py --feature_extractor "path-to-pretrained-fe" --feature_method "fe-method" --ad_model "path-to-pretrained-ad-model" --n_segments "number-of-segments"
The GUI lets you load a video and run the Anomaly Detection code (including feature extraction) and output a video with a graph of the Anomaly Detection prediction below.
Note: The feature extractor and the anomaly detection model must match. Make sure you are using the anomaly detector that was training with the corresponding features.
On-line (via webcam)
Arguments:
- feature_extractor - path to the 3D model to use for feature extraction
- feature_method - which type of model to use for feature extraction (necessary in order to choose the correct pre-processing)
- ad_model - path to the trained anomaly detection model
- clip_length - the length of each video clip (in frames)
python AD_live_prediction.py --feature_extractor "path-to-pretrained-fe" --feature_method "fe-method" --ad_model "path-to-pretrained-ad-model" --clip_length "number-of-frames"
The GUI lets you load a video and run the Anomaly Detection code (including feature extraction) and output a video with a graph of the Anomaly Detection prediction below.
Note: The feature extractor and the anomaly detection model must match. Make sure you are using the anomaly detector that was training with the corresponding features.
*Contrbuted by Peter Overbury of Sussex Universty IISP Group
Cite
@misc{anomaly18cvpr-pytorch,
author = "Eitan Kosman",
title = "Pytorch implementation of Real-World Anomaly Detection in Surveillance Videos",
howpublished = "\url{https://github.com/ekosman/AnomalyDetectionCVPR2018-Pytorch}",
note = "Accessed: 20xx-xx-xx"
}
FAQ
Q: video_demo doesn't show videos
A: Downlaod and install LAVFilters: http://forum.doom9.org/showthread.php?t=156191
Q: What is the meaning of the second column of Train_Annotations.txt?
A: Length of the video in frames. Note that it has not effect on training. It exists because these are the original annotations supplied by the authors.