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STEAD: Spatio-Temporal Efficient Anomaly Detection for Time and Compute Sensitive Applications

PWC This repo is the official implementation of STEAD: Spatio-Temporal Efficient Anomaly Detection for Time and Compute Sensitive Applications

Pretrained models available in the saved_models folder

Extracted X3D Features for UCF-Crime dataset

UCF-Crime X3D Features on Google drive

Feature extraction code also available for modification

Prepare the environment:

    pip install -r requirements.txt

Test: Run

    python test.py

Train: Modify the option.py and run

    python main.py

Citation

@misc{gao2025steadspatiotemporalefficientanomaly,
      title={STEAD: Spatio-Temporal Efficient Anomaly Detection for Time and Compute Sensitive Applications}, 
      author={Andrew Gao and Jun Liu},
      year={2025},
      eprint={2503.07942},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2503.07942}, 
}

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