I'm working on a fall detection system that uses computer vision, but I've hit some hurdles with accuracy. Currently, I'm looking at the height-to-width ratio of people's bounding boxes with a threshold of 1:2, and I'm also tracking changes in torso angle with a threshold of 3. While these tactics work occasionally, they struggle in specific scenarios. For instance, if someone falls toward the camera, the bounding box doesn't shift to a horizontal shape, which messes up the height-to-width ratio analysis. Additionally, when a person falls backward away from the camera, the torso angle often doesn't dip below the set threshold, leading to false alarms. I really need some guidance on how to enhance fall detection in these tricky cases where standard geometric features fall short.
4 Answers
Honestly, I think just going with an accelerometer would be way easier and more effective. It simplifies the whole process—like a thousand times easier!
To improve your detection, consider expanding your algorithm to incorporate 3D space analysis. Instead of only using the body bounding box, you could analyze the head's position relative to the floor for more accurate fall detection.
Have you considered using a standard fall detection sensor like an accelerometer instead of computer vision? It could simplify tracking falls significantly, especially since they are specifically designed for this purpose.
You might want to explore person re-identification (person_reid) methods to better differentiate between fallen and standing people. Keep in mind, this could require you to develop your own model, which could take some time.
Related Questions
Set Wordpress Featured Image Using Javascript
How To Fix PHP Random Being The Same
Why no WebP Support with Wordpress
Replace Wordpress Cron With Linux Cron
Customize Yoast Canonical URL Programmatically
[Centos] Delete All Files And Folders That Contain a String