TR2023-001

Cross-Domain Video Anomaly Detection without Target Domain Adaptation


    •  Aich, A., Peng, K.-C., Roy-Chowdhury, A.K., "Cross-Domain Video Anomaly Detection without Target Domain Adaptation", IEEE Winter Conference on Applications of Computer Vision (WACV), Crandall, D. and Gong, B. and Lee, Y. J. and Souvenir, R. and Yu, S., Eds., DOI: 10.1109/​WACV56688.2023.00261, January 2023, pp. 2578-2590.
      BibTeX TR2023-001 PDF Video Presentation
      • @inproceedings{Aich2023jan,
      • author = {Aich, Abhishek and Peng, Kuan-Chuan and Roy-Chowdhury, Amit K.},
      • title = {Cross-Domain Video Anomaly Detection without Target Domain Adaptation},
      • booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
      • year = 2023,
      • editor = {Crandall, D. and Gong, B. and Lee, Y. J. and Souvenir, R. and Yu, S.},
      • pages = {2578--2590},
      • month = jan,
      • publisher = {IEEE},
      • doi = {10.1109/WACV56688.2023.00261},
      • issn = {2642-9381},
      • isbn = {978-1-6654-9346-8},
      • url = {https://www.merl.com/publications/TR2023-001}
      • }
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  • Research Areas:

    Computer Vision, Machine Learning

Abstract:

Most cross-domain unsupervised Video Anomaly Detection (VAD) works assume that at least few task-relevant target domain training data are available for adaptation from the source to the target domain. However, this requires laborious model- tuning by the end-user who may prefer to have a system that works "out-of-the-box." To address such practical scenarios, we identify a novel target domain (inference-time) VAD task where no target domain training data are available. To this end, we propose a new 'Zero-shot Cross-domain Video Anomaly Detection (zxVAD)' framework that includes a future-frame prediction generative model setup. Different from prior future- frame prediction models, our model uses a novel Normalcy Classifier module to learn the features of normal event videos by learning how such features are different "relatively" to features in pseudo-abnormal examples. A novel Untrained Convolu- tional Neural Network based Anomaly Synthesis module crafts these pseudo-abnormal examples by adding foreign objects in normal video frames with no extra training cost. With our novel relative normalcy feature learning strategy, zxVAD general- izes and learns to distinguish between normal and abnormal frames in a new target domain without adaptation during inference. Through evaluations on common datasets, we show that zxVAD outperformsthestate-of-the-art(SOTA),regardless of whether task-relevant (i.e., VAD) source training data are avail- able or not. Lastly, zxVAD also beats the SOTA methods in inference-time efficiency metrics including the model size, total parameters, GPU energy consumption, and GMACs.

 

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  • Related Publication

  •  Aich, A., Peng, K.-C., Roy-Chowdhury, A.K., "Cross-Domain Video Anomaly Detection without Target Domain Adaptation", arXiv, December 2022.
    BibTeX arXiv
    • @article{Aich2022dec,
    • author = {Aich, Abhishek and Peng, Kuan-Chuan and Roy-Chowdhury, Amit K.},
    • title = {Cross-Domain Video Anomaly Detection without Target Domain Adaptation},
    • journal = {arXiv},
    • year = 2022,
    • month = dec,
    • url = {https://arxiv.org/abs/2212.07010}
    • }