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http://dx.doi.org/10.9708/jksci.2021.26.12.077

Unauthorized person tracking system in video using CNN-LSTM based location positioning  

Park, Chan (Dept. of Computer Science and Engineering, Hoseo University)
Kim, Hyungju (Dept. of Computer Science and Engineering, Hoseo University)
Moon, Nammee (Dept. of Computer Science and Engineering, Hoseo University)
Publication Information
Journal of the Korea Society of Computer and Information / v.26, no.12, 2021 , pp. 77-84 More about this Journal
Abstract
In this paper, we propose a system that uses image data and beacon data to classify authorized and unauthorized perosn who are allowed to enter a group facility. The image data collected through the IP camera uses YOLOv4 to extract a person object, and collects beacon signal data (UUID, RSSI) through an application to compose a fingerprinting-based radio map. Beacon extracts user location data after CNN-LSTM-based learning in order to improve location accuracy by supplementing signal instability. As a result of this paper, it showed an accuracy of 93.47%. In the future, it can be expected to fusion with the access authentication process such as QR code that has been used due to the COVID-19, track people who haven't through the authentication process.
Keywords
CNN-LSTM; Location Positioning; Fingerprinting; Object Recognition; Object Tracking;
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