An Automatic Shoplifting Detection from Surveillance Videos (Student Abstract)

Authors

  • U-Ju Gim Chungbuk National University
  • Jae-Jun Lee Chungbuk National University
  • Jeong-Hun Kim Chungbuk National University
  • Young-Ho Park Sookmyung Women's University
  • Aziz Nasridinov Chungbuk National University

DOI:

https://doi.org/10.1609/aaai.v34i10.7169

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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Published

2020-04-03

How to Cite

Gim, U.-J., Lee, J.-J., Kim, J.-H., Park, Y.-H., & Nasridinov, A. (2020). An Automatic Shoplifting Detection from Surveillance Videos (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 34(10), 13795-13796. https://doi.org/10.1609/aaai.v34i10.7169

Issue

Vol. 34 No. 10: Issue 10: AAAI-20 Student Tracks

Section

Student Abstract Track