Three ffmpeg Filters Looked for a Person Crossing a Still Camera's View. One Found Them
Continuous recording has an obvious cost: hours of a picture in which nothing changes, with the few seconds you care about somewhere inside. The usual answer is motion detection in the camera app, decided at recording time. This piece is about the other answer: keep the continuous recording and ask a laptop afterwards where the picture changed. To see which of ffmpeg's built-in filters can do…
Motion detection often consumes a lot of recording time, with only a few seconds of interest. The typical solution is to enable motion detection in the camera app at the time of recording. However, this article explores an alternative approach: recording continuously and post-processing the footage to identify changes. To test which of ffmpeg's built-in filters can accomplish this, three filters were evaluated against a ninety-second synthetic clip featuring a person-sized dark block crossing the room at a known time.
The clip also included a slow room dimming and a light being switched on. After running the filters with varying settings, it was found that only one filter successfully identified the person across a range of conditions.
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