Abstract
SAND2022-7963 O
The Feature-Based Anomaly Detection System detects anomalies in video data based on features derived from pre-trained image models to avoid the need for training. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
- Developers:
-
Hannasch, David [1][2][3] ; Jones, Jessica [1][2][3] ; Potter, Kevin [1][2][3] ; Garland, Anthony [1][2][3]
- Sandia National Lab. (SNL-CA), Livermore, CA (United States)
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States)
- Release Date:
- 2022-05-05
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Programming Languages:
-
Python
Dockerfile
Shell
- Licenses:
-
MIT License
- Sponsoring Org.:
-
USDOEPrimary Award/Contract Number:NA0003525
- Code ID:
- 94731
- Site Accession Number:
- SCR #2785
- Research Org.:
- Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
- Country of Origin:
- United States
Citation Formats
Hannasch, David, Jones, Jessica, Potter, Kevin, and Garland, Anthony.
Feature-based Anomaly Detection System.
Computer Software.
https://github.com/sandialabs/Feature-based-Anomaly-Detection.
USDOE.
05 May. 2022.
Web.
doi:10.11578/dc.20230622.2.
Hannasch, David, Jones, Jessica, Potter, Kevin, & Garland, Anthony.
(2022, May 05).
Feature-based Anomaly Detection System.
[Computer software].
https://github.com/sandialabs/Feature-based-Anomaly-Detection.
https://doi.org/10.11578/dc.20230622.2.
Hannasch, David, Jones, Jessica, Potter, Kevin, and Garland, Anthony.
"Feature-based Anomaly Detection System." Computer software.
May 05, 2022.
https://github.com/sandialabs/Feature-based-Anomaly-Detection.
https://doi.org/10.11578/dc.20230622.2.
@misc{
doecode_94731,
title = {Feature-based Anomaly Detection System},
author = {Hannasch, David and Jones, Jessica and Potter, Kevin and Garland, Anthony},
abstractNote = {SAND2022-7963 O
The Feature-Based Anomaly Detection System detects anomalies in video data based on features derived from pre-trained image models to avoid the need for training. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.},
doi = {10.11578/dc.20230622.2},
url = {https://doi.org/10.11578/dc.20230622.2},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20230622.2}},
year = {2022},
month = {may}
}