A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised
  Video Anomaly Detection
Anas Al-lahham Nurbek Tastan Zaigham Zaheer Karthik Nandakumar
Detection of anomalous events in videos is an important problem in
applications such as surveillance. Video anomaly detection (VAD) is
well-studied in the one-class classification (OCC) and weakly supervised (WS)
settings. However, fully unsupervised (US) video anomaly detection methods,
which learn a complete system without any annotation or human supervision, have
not been explored in depth. This is because the lack of any ground truth
annotations significantly increases the magnitude of the VAD challenge. To
address this challenge, we propose a simple-but-effective two-stage
pseudo-label generation framework that produces segment-level (normal/anomaly)
pseudo-labels, which can be further used to train a segment-level anomaly
detector in a supervised manner. The proposed coarse-to-fine pseudo-label
(C2FPL) generator employs carefully-designed hierarchical divisive clustering
and statistical hypothesis testing to identify anomalous video segments from a
set of completely unlabeled videos. The trained anomaly detector can be
directly applied on segments of an unseen test video to obtain segment-level,
and subsequently, frame-level anomaly predictions. Extensive studies on two
large-scale public-domain datasets, UCF-Crime and XD-Violence, demonstrate that
the proposed unsupervised approach achieves superior performance compared to
all existing OCC and US methods , while yielding comparable performance to the
state-of-the-art WS methods.