A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit <a rel="external noopener" href="https://arxiv.org/pdf/2103.10502v2.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
<span class="release-stage" >pre-print</span>
Video anomaly detection has proved to be a challenging task owing to its unsupervised training procedure and high spatio-temporal complexity existing in real-world scenarios. In the absence of anomalous training samples, state-of-the-art methods try to extract features that fully grasp normal behaviors in both space and time domains using different approaches such as autoencoders, or generative adversarial networks. However, these approaches completely ignore or, by using the ability of deep<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2103.10502v2">arXiv:2103.10502v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/g3uhxx7kgbgabdsyj5b76be6fm">fatcat:g3uhxx7kgbgabdsyj5b76be6fm</a> </span>
more »... works in the hierarchical modeling, poorly model the spatio-temporal interactions that exist between objects. To address this issue, we propose a novel yet efficient method named Ano-Graph for learning and modeling the interaction of normal objects. Towards this end, a Spatio-Temporal Graph (STG) is made by considering each node as an object's feature extracted from a real-time off-the-shelf object detector, and edges are made based on their interactions. After that, a self-supervised learning method is employed on the STG in such a way that encapsulates interactions in a semantic space. Our method is data-efficient, significantly more robust against common real-world variations such as illumination, and passes SOTA by a large margin on the challenging datasets ADOC and Street Scene while stays competitive on Avenue, ShanghaiTech, and UCSD.
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