Abstract
Real-time detection of irregularities in visual data is very invaluable and useful in many prospective applications including surveillance, patient monitoring systems, etc. With the surge of deep learning methods in the recent years, researchers have tried a wide spectrum of methods for different applications. However, for the case of irregularity or anomaly detection in videos, training an end-to-end model is still an open challenge, since often irregularity is not well-defined and there are not enough irregular samples to use during training. In this paper, inspired by the success of generative adversarial networks (GANs) for training deep models in unsupervised or self-supervised settings, we propose an end-to-end deep network for detection and fine localization of irregularities in videos (and images). Our proposed architecture is composed of two networks, which are trained in competing with each other while collaborating to find the irregularity. One network works as a pixel-level irregularity \(\mathcal {I}\)npainter, and the other works as a patch-level \(\mathcal {D}\)etector. After an adversarial self-supervised training, in which \(\mathcal {I}\) tries to fool \(\mathcal {D}\) into accepting its inpainted output as regular (normal), the two networks collaborate to detect and fine-segment the irregularity in any given testing video. Our results on three different datasets show that our method can outperform the state-of-the-art and fine-segment the irregularity.
M. Sabokrou, M. Pourreza and M. Fayyaz–Contributed equally.
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Acknowledgements
This research was in part supported by a grant from IPM (No. CS1396-5-01). Mohsen Fayyaz and Juergen Gall have been financially supported by the DFG project GA 1927/4-1 (Research Unit FOR 2535) and the ERC Starting Grant ARCA (677650).
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Sabokrou, M. et al. (2019). AVID: Adversarial Visual Irregularity Detection. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11366. Springer, Cham. https://doi.org/10.1007/978-3-030-20876-9_31
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