Luggage falling-off recognition based on combined motion model in surveillance video

Rongyong Zhao; Bingyu Wei; Yanhua Wu; Rahman Arifur; Wenjie Zhu; Miyuan Li; Yunlong Ma

doi:10.1117/12.3026777

27 March 2024 Luggage falling-off recognition based on combined motion model in surveillance video

Rongyong Zhao, Bingyu Wei, Yanhua Wu, Rahman Arifur, Wenjie Zhu, Miyuan Li, Yunlong Ma

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 1310526 (2024) https://doi.org/10.1117/12.3026777
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

Research on luggage of passengers has increased recently in the field of security management in transportation hubs. Previous research primarily focused on the impact of luggage on crowd evacuation in the case of emergencies. How to recognize a luggage, and how they impact the movement of crowd still worth further and systematic study. The abnormal states of luggage (e.g., falling off, tail flicking and acceleration) can perturb the natural march of crowd. This study proposes an approach of luggage falling-off recognition involving computer vision technology. This approach considers the movement of passenger and luggage as a combined motion. Experiment results indicate that the proposed approach in this study can recognize the falling-off state of luggage effectively and can provide a practicable technique reference for carried object analysis in transportation hubs.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Rongyong Zhao, Bingyu Wei, Yanhua Wu, Rahman Arifur, Wenjie Zhu, Miyuan Li, and Yunlong Ma "Luggage falling-off recognition based on combined motion model in surveillance video", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 1310526 (27 March 2024); https://doi.org/10.1117/12.3026777

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KEYWORDS

Video

Video surveillance

Motion models

Surveillance

Transportation

Cameras

Computer vision technology

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