A system like that would be surprisingly hard to build. The problem wouldn't be the ML algorithms - it would be just about everything else. A few things you nee

A system like that would be surprisingly hard to build. The problem wouldn't be the ML algorithms - it would be just about everything else. A few things you need to solve robustly to build your counter: 1. The "same background" doesn't really exist for most cameras in most settings. Changes in illumination alone will make segmenting the background tricky. Moving objects in the scene will also be hard - think fountains and trees in the wind. Google for "foreground-background segmentation" to see some papers on this. 2. I haven't seen anyone use recent ML algorithms with less than high quality images. That may not matter, but it could matter a lot. 3. Extending recent ML algorithms to work with video at a high enough frame rate to be useful (10Hz at a minimum) may or may not be easy. I'm sure that what you're proposing could be done. But I think that the number of small annoyances you'd hit would probably discourage most people who aren't treating the problem as a research exercise in Computer Vision.