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Detecting movement of the camera itself

The goal is to detect whether or not the computer camera is moving. Reaction needs to be fast. If people are moving in front of the camera, it shouldn't confuse the camera.

I tried using mobilenet SSD on the images, masking away people detected in the image, then using background subtraction to determine how much movement there is. It works okay, but it is slow. Problems ; mobilenet SSD is slow, and background subtraction isn't always that accurate (if I go stand in front of the window the change in lighting makes it look like there was a lot of movement).

I tried considering only corners but what's in the corners is usually considered background by the algorithm.

I tried doing feature matching to detect if keypoints all moved in a similar direction (in the corners of the image, for example).

More ideas I've yet to try ; optical flow (no experience with this), camera calibration with RANSAC (no experience either) and training my own machine learning model (based on 1000fps Human Segmentation with Deep Convolutional Neural Networks by C. Song).

Does anyone here have any other ideas that might increase the accuracy of the algorithm? Accuracy and speed of detection are equally as important.