TY  - JOUR
PY  - 2022//
TI  - CamNuvem: a robbery dataset for video anomaly detection
JO  - Sensors (Basel)
A1  - de Paula, Davi D.
A1  - Salvadeo, Denis H. P.
A1  - de Araujo, Darlan M. N.
SP  - e10016
EP  - e10016
VL  - 22
IS  - 24
N2  - (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.<p />  <p>Language: en</p>
LA  - en
SN  - 1424-8220
UR  - http://dx.doi.org/10.3390/s222410016
ID  - ref1
ER  -