Abstract
Image recognition technology based on deep learning has made great progress, which makes object detection technology work in many fields. The number of elderly people in China has risen year by year, proclaiming the arrival of an aging society. “The old man can’t fall” is a consensus. Using object detection algorithm to detect the fall of the elderly is a research hotspot in the field of object detection. Through the analysis of the object detection algorithm and the object tracking algorithm, Deep-sort and YOLOv3 algorithms are used to achieve the real-time fall detection of the surveillance video. The experimental results prove that combined with YOLOv3 and the Deep-sort algorithms can detect the fall of the elderly.
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Acknowledgment
This work is partially supported by Hainan Key R&D Program Projects (No. ZDYF2018017); Supported by Haikou Key Science and Technology Plan Project (No. 2017039); Supported by Hainan Natural Science Foundation Project (No.: 618MS028); Supported by National Natural Science Foundation of China Project (No. 61573356).
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Qiu, Z., Liang, X., Chen, Q., Huang, X., Wang, Y. (2020). Old Man Fall Detection Based on Surveillance Video Object Tracking. In: Shen, H., Sang, Y. (eds) Parallel Architectures, Algorithms and Programming. PAAP 2019. Communications in Computer and Information Science, vol 1163. Springer, Singapore. https://doi.org/10.1007/978-981-15-2767-8_15
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DOI: https://doi.org/10.1007/978-981-15-2767-8_15
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