Approaches Toward Physical and General Video Anomaly Detection

TL;DR

This paper explores methods for detecting anomalies in videos with mechanical or physical irregularities, introduces a new dataset called PHANTOM, and evaluates baseline approaches for this challenging task.

Contribution

It introduces the PHANTOM dataset for physical anomaly detection and assesses baseline methods, addressing a gap in anomaly detection research beyond surveillance videos.

Findings

Baseline approaches show potential but highlight the need for more advanced methods.

PHANTOM dataset provides a new benchmark for physical anomaly detection.

Highly variable scenes pose significant challenges for anomaly detection.

Abstract

In recent years, many works have addressed the problem of finding never-seen-before anomalies in videos. Yet, most work has been focused on detecting anomalous frames in surveillance videos taken from security cameras. Meanwhile, the task of anomaly detection (AD) in videos exhibiting anomalous mechanical behavior, has been mostly overlooked. Anomaly detection in such videos is both of academic and practical interest, as they may enable automatic detection of malfunctions in many manufacturing, maintenance, and real-life settings. To assess the potential of the different approaches to detect such anomalies, we evaluate two simple baseline approaches: (i) Temporal-pooled image AD techniques. (ii) Density estimation of videos represented with features pretrained for video-classification. Development of such methods calls for new benchmarks to allow evaluation of different possible approaches. We introduce the Physical Anomalous Trajectory or Motion (PHANTOM) dataset, which contains six different video classes. Each class consists of normal and anomalous videos. The classes differ in the presented phenomena, the normal class variability, and the kind of anomalies in the videos. We also suggest an even harder benchmark where anomalous activities should be spotted on highly variable scenes.