Xinchi Zhao, Anming Hu, Wei He. Fall Detection Based on Convolutional Neural Network and XGBoost[J]. Laser & Optoelectronics Progress, 2020, 57(16): 161024
- Laser & Optoelectronics Progress
- Vol. 57, Issue 16, 161024 (2020)
Fig. 1. Overall flow chart of algorithm
Fig. 2. Architecture of SE block
Fig. 3. Architecture of human pose estimation
Fig. 4. Schematic diagram of partial eigenvalue selection. (a) Some joints and joint angles; (b) body relative position vector
Fig. 5. Training samples (standing). (a) Walking in oblique direction; (b) backward walking; (c) lateral walking; (d) front standing
Fig. 6. Training sample (falling). (a) Front half fall; (b) side half fall; (c) lie; (d) prostration
Fig. 7. Training sample (sitting). (a) Sitting posture of left; (b) sitting posture of right
Fig. 8. Examples of pose estimation results. (a) Fall posture bone detection; (b) sitting posture bone detection; (c) standing posture bone detection; (d) coordinate distribution of 17 joints of standing posture of human body
Fig. 9. First subtree of XGBoost
Fig. 10. Test results of actual scene. (a) Half fall; (b) fall on one side
Fig. 11. Comparison of different algorithms. (a) Algorithm in this paper under the same posture; (b) poor posture detected in Ref. [10]
|
Table 1. Experimental results
| |||||||||||||||||||||||||
Table 2. Confusion matrix
|
Table 3. Classification evaluation indexes
|
Table 4. Comparison of results%
Set citation alerts for the article
Please enter your email address
© Copyright 2018-2021 | Chinese Laser Press. All Rights Reserved 沪ICP备15018463号-20