Official implementation of "Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detection" (ICCV Workshops 2021: RSL-CV).

Related tags

Deep LearningSTEAL
Overview

Official PyTorch implementation of "Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detection"

This is the implementation of the paper "Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detection" (ICCV Workshops 2021: RSL-CV).

Paper || Presentation Video

Dependencies

  • Python 3.6
  • PyTorch = 1.7.0
  • Numpy
  • Sklearn

Datasets

Download the datasets into dataset folder, like ./dataset/ped2/, ./dataset/avenue/, ./dataset/shanghai/

Training

git clone https://github.com/aseuteurideu/STEAL
  • Training baseline
python train.py --dataset_type ped2
  • Training STEAL Net
python train.py --dataset_type ped2 --pseudo_anomaly_jump 0.2 --jump 2 3 4 5

Select --dataset_type from ped2, avenue, or shanghai.

For more details, check train.py

Pre-trained model

Model Dataset AUC Weight
Baseline Ped2 92.5% [ drive ]
Baseline Avenue 81.5% [ drive ]
Baseline ShanghaiTech 71.3% [ drive ]
STEAL Net Ped2 98.4% [ drive ]
STEAL Net Avenue 87.1% [ drive ]
STEAL Net ShanghaiTech 73.7% [ drive ]

Evaluation

  • Test the model
python evaluate.py --dataset_type ped2 --model_dir path_to_weight_file.pth
  • Test the model and save result image
python evaluate.py --dataset_type ped2 --model_dir path_to_weight_file.pth --img_dir folder_path_to_save_image_results
  • Test the model and generate demonstration video frames
python evaluate.py --dataset_type ped2 --model_dir path_to_weight_file.pth --vid_dir folder_path_to_save_video_results

Then compile the frames into video. For example, to compile the first video in ubuntu:

ffmpeg -framerate 10 -i frame_00_%04d.png -c:v libx264 -profile:v high -crf 20 -pix_fmt yuv420p video_00.mp4

Bibtex

@InProceedings{Astrid_2021_ICCV,
    author    = {Astrid, Marcella and Zaheer, Muhammad Zaigham and Lee, Seung-Ik},
    title     = {Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detection},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops},
    month     = {October},
    year      = {2021},
    pages     = {207-214}
}

Acknowledgement

The code is built on top of code provided by Park et al. [ github ] and Gong et al. [ github ]

Owner
Marcella Astrid
PhD candidate at University of Science and Technology, ETRI campus, South Korea
Marcella Astrid
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