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

2021-07-31 14:28:29
Kayleigh Boekhoudt, Alina Matei, Maya Aghaei, Estefanía Talavera
arXiv_CV
       
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.

Abstract (translated)

URL

https://arxiv.org/abs/2108.00246

PDF

https://arxiv.org/pdf/2108.00246.pdf

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