CamNuvem: A Robbery Dataset for Video Anomaly Detection

Abstract

(1) Background: The research area of video surveillance anomaly detection aims to automatically detect the moment when a video surveillance camera captures something that does not fit the normal pattern. This is a difficult task, but it is important to automate, improve, and lower the cost of the detection of crimes and other accidents. The UCF–Crime dataset is currently the most realistic crime dataset, and it contains hundreds of videos distributed in several categories; it includes a robbery category, which contains videos of people stealing material goods using violence, but this category only includes a few videos. (2) Methods: This work focuses only on the robbery category, presenting a new weakly labelled dataset that contains 486 new real–world robbery surveillance videos acquired from public sources. (3) Results: We have modified and applied three state–of–the–art video surveillance anomaly detection methods to create a benchmark for future studies. We showed that in the best scenario, taking into account only the anomaly videos in our dataset, the best method achieved an AUC of 66.35%. When all anomaly and normal videos were taken into account, the best method achieved an AUC of 88.75%. (4) Conclusion: This result shows that there is a huge research opportunity to create new methods and approaches that can improve robbery detection in video surveillance.

Keywords

• video anomaly detection
• dataset
• human behaviour analysis
• weakly supervised
• activity recognition
• video surveillance