Fig. 1. Illustration of multiple people recognition and positioning with multiple cameras based on “vibration signals” from skin surfaces

Fig. 2. Principle derivation of difference and synchronization of “vibration signals” from skin surfaces

Fig. 3. Validation of synchronization and difference. (a) Experimental scene 1; (b) experimental scene 2; (c) example of IPPG signal acquisition for different skin areas of the same object under the same camera; (d) IPPG signal acquisition for different skin areas of different objects under the same camera

Fig. 4. IPPG signal acquisition for the same skin area of different objects under multiple cameras

Fig. 5. Flow chart of human body recognition and positioning system based on multiple cameras

Fig. 6. Acquisition of “vibration signals” from human skin surfaces

Fig. 7. Positioning with three cameras. (a) Top view of experimental scene; (b) principle diagram of positioning

Fig. 8. One frame of acquired video with three cameras and tracked skin areas

Fig. 9. IPPG signals acquired by camera 3

Fig. 10. IPPG signals acquired by cameras 2 and 3

Fig. 11. Multiple coordinate calculation results of objects A and B

Fig. 12. Experimental scene of 5 objects for recognition and positioning with 3 cameras. (a) Experimental scene for multi-object recognition and positioning; (b) top view of scene; (c) one frame of acquired video by three cameras and skin areas

Fig. 13. Schematic of accuracy area distribution of camera

Fig. 14. Coordinate calculation results of 5 objects

Table 1. Method comparison for multiple people recognition and positioning with multiple cameras

Table 2. Similarity of IPPG signals acquired by camera 3

Table 3. Similarity of IPPG signals acquired by cameras 2 and 3

Table 4. Classification results of similarity comparison and normalization

Table 5. Precision and recall rates of classification results of similarity comparison and normalization