Abstract
This paper proposed an accurate fall detection algorithm based on the feature of whole human body. The feature is extracted from convolutional neural network. The implementation of algorithm is integrated into a hardware system based on a visual mobile robot platform. To ensure the robustness and flexibility of algorithm in actual situation, a set of systemic strategies was applied on mobile robot. Finally, sufficient experiments on public dataset were conduct on our algorithm. Moreover, in a real indoor scene, experiment results proved the efficiency and precision of the designed fall detection system.
X. Kang—This work was supported by National Natural Science Foundation of China under grant of No. 61471343, No. 61701036, Fundamental Research Funds for the Central Universities No. 2017RC52.
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Notes
- 1.
In this work, we justly identify fall or not, the other postures would not be identified.
- 2.
We will release a new fallen person dataset in future. It has 2000 pictures containing different scenarios, different persons and different postures.
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Sun, P., Ming, A., Yao, C., Kang, X. (2018). Fall Detection System Based on Mobile Robot. In: Wang, Y., Jiang, Z., Peng, Y. (eds) Image and Graphics Technologies and Applications. IGTA 2018. Communications in Computer and Information Science, vol 875. Springer, Singapore. https://doi.org/10.1007/978-981-13-1702-6_26
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