HR-Crime: Human-Related Anomaly Detection in Surveillance Videos
This work addresses the need for efficient anomaly detection in surveillance for security applications, but it is incremental as it builds on existing datasets and methods.
The paper tackled the problem of detecting human-related anomalies in surveillance videos by introducing HR-Crime, a subset of the UCF-Crime dataset, and presented baseline analysis using state-of-the-art feature extraction techniques, with the dataset and pipeline made publicly available.
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.