Ano-Graph: Learning Normal Scene Contextual Graphs to Detect Video Anomalies

TL;DR

Ano-Graph introduces a novel approach for video anomaly detection by modeling object interactions through spatio-temporal graphs, leveraging self-supervised learning to improve robustness and outperform existing methods on multiple datasets.

Contribution

The paper proposes Ano-Graph, a new method that models normal object interactions with spatio-temporal graphs and self-supervised learning, enhancing anomaly detection performance.

Findings

Outperforms state-of-the-art on ADOC and Street Scene datasets.

More robust against illumination variations.

Data-efficient and competitive on multiple benchmarks.

Abstract

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 networks 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.