Evaluating the Effectiveness of Video Anomaly Detection in the Wild: Online Learning and Inference for Real-world Deployment

TL;DR

This paper evaluates the real-world effectiveness of video anomaly detection models using an online learning framework that allows continuous adaptation to new environments, highlighting their potential for practical deployment.

Contribution

It introduces an online learning framework for VAD that enables models to adapt in real-time to diverse, unseen environments, addressing a key gap in current research.

Findings

Models retain 89.39% of their original effectiveness after adaptation.

Online learning improves model robustness across different domains.

The framework demonstrates practical potential for real-world VAD deployment.

Abstract

Video Anomaly Detection (VAD) identifies unusual activities in video streams, a key technology with broad applications ranging from surveillance to healthcare. Tackling VAD in real-life settings poses significant challenges due to the dynamic nature of human actions, environmental variations, and domain shifts. Many research initiatives neglect these complexities, often concentrating on traditional testing methods that fail to account for performance on unseen datasets, creating a gap between theoretical models and their real-world utility. Online learning is a potential strategy to mitigate this issue by allowing models to adapt to new information continuously. This paper assesses how well current VAD algorithms can adjust to real-life conditions through an online learning framework, particularly those based on pose analysis, for their efficiency and privacy advantages. Our proposed framework enables continuous model updates with streaming data from novel environments, thus mirroring actual world challenges and evaluating the models' ability to adapt in real-time while maintaining accuracy. We investigate three state-of-the-art models in this setting, focusing on their adaptability across different domains. Our findings indicate that, even under the most challenging conditions, our online learning approach allows a model to preserve 89.39% of its original effectiveness compared to its offline-trained counterpart in a specific target domain.