  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.
