AnomalyDetectionCVPR2018-Pytorch

Pytorch version of - https://github.com/WaqasSultani/AnomalyDetectionCVPR2018

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! 😏

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 env create -f environment.yml

conda activate adCVPR18

C3D Weights 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

Precomputed Features

Can be downloaded from:

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"

Using my pre-trained model after 40K iterations, I achieve this following performance on the test-set. I'm aware that the current model doesn't achieve AUC of 0.75 as reported in the original paper. This can be caused by different weights of the C3D model.

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.

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.

*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

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