HR-Crime: Human-Related Anomaly Detection in Surveillance Videos

TL;DR

This paper introduces HR-Crime, a new dataset subset for human-related anomaly detection in surveillance videos, utilizing state-of-the-art feature extraction techniques and providing baseline analysis for future research.

Contribution

The paper presents HR-Crime, a specialized dataset for human-related anomalies, along with a feature extraction pipeline and baseline analysis, advancing research in surveillance anomaly detection.

Findings

HR-Crime dataset is suitable for human-related anomaly detection.

A feature extraction pipeline for anomaly detection is developed.

Baseline analysis demonstrates the dataset's utility for future research.

Abstract

The automatic detection of anomalies captured by surveillance settings is essential for speeding the otherwise laborious approach. To date, UCF-Crime is the largest available dataset for automatic visual analysis of anomalies and consists of real-world crime scenes of various categories. In this paper, we introduce HR-Crime, a subset of the UCF-Crime dataset suitable for human-related anomaly detection tasks. We rely on state-of-the-art techniques to build the feature extraction pipeline for human-related anomaly detection. Furthermore, we present the baseline anomaly detection analysis on the HR-Crime. HR-Crime as well as the developed feature extraction pipeline and the extracted features will be publicly available for further research in the field.