  Unsupervised (US) video anomaly detection (VAD) in surveillance applications
is gaining more popularity recently due to its practical real-world
applications. As surveillance videos are privacy sensitive and the availability
of large-scale video data may enable better US-VAD systems, collaborative
learning can be highly rewarding in this setting. However, due to the extremely
challenging nature of the US-VAD task, where learning is carried out without
any annotations, privacy-preserving collaborative learning of US-VAD systems
has not been studied yet. In this paper, we propose a new baseline for anomaly
detection capable of localizing anomalous events in complex surveillance videos
in a fully unsupervised fashion without any labels on a privacy-preserving
participant-based distributed training configuration. Additionally, we propose
three new evaluation protocols to benchmark anomaly detection approaches on
various scenarios of collaborations and data availability. Based on these
protocols, we modify existing VAD datasets to extensively evaluate our approach
as well as existing US SOTA methods on two large-scale datasets including
UCF-Crime and XD-Violence. All proposed evaluation protocols, dataset splits,
and codes are available here: https://github.com/AnasEmad11/CLAP
