(cache)Application of YOLO Deep Learning Model for Real Time Abandoned Baggage Detection | IEEE Conference Publication | IEEE Xplore

Application of YOLO Deep Learning Model for Real Time Abandoned Baggage Detection

Publisher: IEEE

Abstract:

We proposed an abandoned-baggage detection system that the baggage was left in public places for security reasons, i.e., subway stations. The proposed system applied the ...View more

Abstract:

We proposed an abandoned-baggage detection system that the baggage was left in public places for security reasons, i.e., subway stations. The proposed system applied the YOLO deep learning model for object detection, and presented a GUI for supporting a parameter setting. With this GUI, the detection system will be invariant to lighting and camera position.
Date of Conference: 09-12 October 2018
Date Added to IEEE Xplore: 13 December 2018
ISBN Information:
Print on Demand(PoD) ISSN: 2378-8143
Publisher: IEEE
Conference Location: Nara, Japan

I. Introduction

The idea about problem of abandoned luggage detection was established by Sugrue and Davies in around 2005 [Sugrue and Davies 2005]. Then, there are many techniques and systems were proposed to solve this problem until present. For example Kevin et al. [Kevin et al. 2006] proposed the Macov Chain Monte Carlo tracking (MCMC). Guler et al. [Guler et al. 2007] proposed the Motion History Tracking. The system of alarming and finding the owner of left baggage was proposed by Chang et al.[Chang et al. 2010].

References

References is not available for this document.