Abstract
This paper focuses on developing a social distance alert system using pose estimation for smart edge devices. Recently, with the rapid development of the Deep Learning model for computer vision, a vision-based automatic real-time warning system for social distance becomes an emergent issue. In this study, different from previous works, we propose a new framework for distance measurement using pose estimation. Moreover, the system is developed on smart edge devices, which is able to deal with moving cameras instead of fixed cameras of surveillance systems. Specifically, our method includes three main processes, which are video pre-processing, pose estimation, and object distance estimation. The experiment on coral board, an AI accelerator device, provides promising results of our proposed method in which the accuracies are able to achieve more than 85% from different datasets.
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Notes
- 1.
Coral. Available online: https://www.coral.ai/ (accessed on 22 November 2020).
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Acknowledgment
This work was supported by the New Industry Promotion Program (1415166913, Development of Front/Side Camera Sensor for Autonomous Vehicle) funded by the Ministry of Trade, Industry Energy (MOTIE, Korea).
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To, HT., Bui, KH.N., Le, VD., Bui, TC., Li, WS., Cha, S.K. (2021). Real-Time Social Distancing Alert System Using Pose Estimation on Smart Edge Devices. In: Hong, TP., Wojtkiewicz, K., Chawuthai, R., Sitek, P. (eds) Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2021. Communications in Computer and Information Science, vol 1371. Springer, Singapore. https://doi.org/10.1007/978-981-16-1685-3_24
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