Latest Deep Learning Techniques for Fall Detection in Monitoring Real-Time Video Data
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-0037-0_27
Reference16 articles.
1. Agrawal M, Agrawal S (2020) Suspicious event detection in real-time video surveillance system. In: Social networking and computational intelligence, lecture notes in networks and systems, vol 100. Springer Singapore, Singapore, pp 59–516. https://doi.org/10.1007/978-981-15-2071-6_40
2. Soni PK, Choudhary A (2019) Automated fall detection from a camera using support vector machine. In: 2019 second international conference on advanced computational and communication paradigms (ICACCP). IEEE, Gangtok, India, pp 1–6. https://doi.org/10.1109/ICACCP.2019.8882966
3. Alzahrani MS, Jarraya SK, Ben-Abdallah H, Ali MS (2019) Comprehensive evaluation of skeleton features-based fall detection from Microsoft Kinect v2. SIViP 13:1431–1439. https://doi.org/10.1007/s11760-019-01490-9
4. Ramanujam E, Padmavathi S (2019) A vision-based posture monitoring system for the elderly using intelligent fall detection technique. Springer Nature Switzerland AG, Sequence No 11, pp 249–269. https://doi.org/10.1007/978-3-030-04173-1_11
5. Li X, Pang T, Liu W, Wang T (2017) Fall detection for elderly person care using convolutional neural networks. In: 2017 10th international Congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI). Shanghai, China, pp 1–6. https://doi.org/10.1109/CISP-BMEI.2017.8302004
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