An Automatic Shoplifting Detection from Surveillance Videos (Student Abstract)

U-Ju Gim, Jae-Jun Lee, Jeong-Hun Kim, Young-Ho Park, Aziz Nasridinov

AAAI 2020 pp. 13795-13796

doi:10.1609/AAAI.V34I10.7169 /aaai/2020/gim2020aaai-automatic/

Abstract

The use of closed circuit television (CCTV) surveillance devices is increasing every year to prevent abnormal behaviors, including shoplifting. However, damage from shoplifting is also increasing every year. Thus, there is a need for intelligent CCTV surveillance systems that ensure the integrity of shops, despite workforce shortages. In this study, we propose an automatic detection system of shoplifting behaviors from surveillance videos. Instead of extracting features from the whole frame, we use the Region of Interest (ROI) optical-flow fusion network to highlight the necessary features more accurately.

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Text

Gim et al. "An Automatic Shoplifting Detection from Surveillance Videos (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I10.7169

Markdown

[Gim et al. "An Automatic Shoplifting Detection from Surveillance Videos (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/gim2020aaai-automatic/) doi:10.1609/AAAI.V34I10.7169

BibTeX

@inproceedings{gim2020aaai-automatic,
  title     = {{An Automatic Shoplifting Detection from Surveillance Videos (Student Abstract)}},
  author    = {Gim, U-Ju and Lee, Jae-Jun and Kim, Jeong-Hun and Park, Young-Ho and Nasridinov, Aziz},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2020},
  pages     = {13795-13796},
  doi       = {10.1609/AAAI.V34I10.7169},
  url       = {https://mlanthology.org/aaai/2020/gim2020aaai-automatic/}
}