With background subtraction we have got the foreground detected a.k.a. the objects of interest. What should we do with this information?

Activity detection

There are different methods to monitor people. Activity detection is used to determinate what activity the person in the video is performing. In the static analysis the persons posture is analyzed at a specific time. A posture is a good indicator of what the person is doing e.g. lying, standing or sitting. This information alone is not very useful. That is why in the dynamic analysis the outcome of the static approach is combined to the earlier static approach outcomes. In this way we can analyze movement patterns. If the person was standing in the last frame and in the current frame is detected as lying, the person probably have suffered from a fall.

Well in the real world this is not as easy as it looks. One study shows that there are three features that usually occur when a person falls. The incident will happen in a short time period, typically in a range of 0.4-0.8 seconds. The persons centroid changes rapidly and significantly. And last the vertical projection of the person changes significantly.

Position and motion analysis

While posture analysis is a good way to detect the persons state it is hard for it to detect what activity, more specific than just sitting, standing or lying, the person is performing. That is why the persons position could be used to determine what ADL or IADL the person is currently performing. With this technique the daily routines could be monitored and taught to the system and if something abnormal is detected, it could create an alarm.

Combination

Because presented methods does not always achieve the sensitivity needed for a robust system these methods could be combined. The results from static analysis, dynamic analysis, position and motion analysis can be combined with simple AND or OR rules. The final decision could also be generated with combining each output and the certainty of it as a weighted result to create a maybe more robust solution.

Currently the system only detects only if a person is not moving enough in a time period. These are the features that i am currently developing to the second version of this product.