Real world Anomaly Detection in Surveillance Videos : Pytorch RE-Implementation

This repository is a re-implementation of "Real-world Anomaly Detection in Surveillance Videos" with pytorch. As a result of our re-implementation, we achieved a much higher AUC than the original implementation.

Ours full code available at CODE

Datasets

Download following data link and unzip under your $DATA_ROOT_DIR. /workspace/DATA/UCF-Crime/all_rgbs

• Directory tree

  
  DATA/
      UCF-Crime/ 
          ../all_rgbs
              ../~.npy
          ../all_flows
              ../~.npy
      train_anomaly.txt
      train_normal.txt
      test_anomaly.txt
      test_normal.txt

## train-test script

python main.py

## Reslut

| METHOD | DATASET | AUC | 
|:--------:|:--------:|:--------:|
| Original paper(C3D two stream) | UCF-Crimes | 75.41 |
| [RTFM](https://arxiv.org/pdf/2101.10030.pdf) (I3D RGB) | UCF-Crimes | 84.03 |
| Ours Re-implementation (I3D two stream) | UCF-Crimes | 84.45 |

## Visualization

<table>
  <tr>
    <td><img alt="" src="https://github.com/seominseok0429/Real-world-Anomaly-Detection-in-Surveillance-Videos-pytorch/raw/main/sam.gif" /></td> <td><img alt="" src="https://github.com/seominseok0429/Real-world-Anomaly-Detection-in-Surveillance-Videos-pytorch/raw/main/result.png" height="280" width="400" />
  <tr>
</table>

## Acknowledgment

This code is heavily borrowed from [Learning to Adapt to Unseen Abnormal Activities under Weak Supervision](https://github.com/junha-kim/Learning-to-Adapt-to-Unseen-Abnormal-Activities) and [AnomalyDetectionCVPR2018](https://github.com/WaqasSultani/AnomalyDetectionCVPR2018).