Is your feature request related to a problem? Please describe.
Users report that fall detection triggers false positives when there are multiple persons in view. See attached screenshots.
This is a known issue for single person PoseNet models.
Describe the solution you'd like
A sophisticated solutions would be to implement multiple person tracking in a sequence of video frames.
An interim practical improvement would be to discard frames with multiple persons. This is a reasonable approach because:
Fall detector is critically important for situations when elderly people fall while they are alone and unattended by caregivers.
If there are multiple people in a room, then triggering a fall alert is less helpful, because presumably these people can help each other and make emergency calls as needed.
For the interim solution we can use a multi person posenet model and simply discard frames when the model detects multiple persons in a frame. That will avoid potential false fall detections. Testing shows that multi-person posenet models take slightly more CPU time for inference than single person models (2-3 fps vs 3-4 fps on rpi4), which is a reasonable tradeoff since we only need 1fps for fall detection.
Describe alternatives you've considered
A sophisticated solutions would be to implement multiple person tracking in a sequence of video frames. Respectively track and detect falls for each individual person as they move between frames.
Additional context
Screenshots from users with false positives below.
Is your feature request related to a problem? Please describe. Users report that fall detection triggers false positives when there are multiple persons in view. See attached screenshots. This is a known issue for single person PoseNet models.
Describe the solution you'd like A sophisticated solutions would be to implement multiple person tracking in a sequence of video frames.
An interim practical improvement would be to discard frames with multiple persons. This is a reasonable approach because:
For the interim solution we can use a multi person posenet model and simply discard frames when the model detects multiple persons in a frame. That will avoid potential false fall detections. Testing shows that multi-person posenet models take slightly more CPU time for inference than single person models (2-3 fps vs 3-4 fps on rpi4), which is a reasonable tradeoff since we only need 1fps for fall detection.
Describe alternatives you've considered A sophisticated solutions would be to implement multiple person tracking in a sequence of video frames. Respectively track and detect falls for each individual person as they move between frames.
Additional context Screenshots from users with false positives below.