Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline
Journal:
arXiv
Published Date:
Jun 5, 2025
Abstract
Video anomaly detection (VAD) is crucial in scenarios such as surveillance
and autonomous driving, where timely detection of unexpected activities is
essential. Although existing methods have primarily focused on detecting
anomalous objects in videos -- either by identifying anomalous frames or
objects -- they often neglect finer-grained analysis, such as anomalous pixels,
which limits their ability to capture a broader range of anomalies. To address
this challenge, we propose a new framework called Track Any Anomalous Object
(TAO), which introduces a granular video anomaly detection pipeline that, for
the first time, integrates the detection of multiple fine-grained anomalous
objects into a unified framework. Unlike methods that assign anomaly scores to
every pixel, our approach transforms the problem into pixel-level tracking of
anomalous objects. By linking anomaly scores to downstream tasks such as
segmentation and tracking, our method removes the need for threshold tuning and
achieves more precise anomaly localization in long and complex video sequences.
Experiments demonstrate that TAO sets new benchmarks in accuracy and
robustness. Project page available online.
