The field of computer vision is ever-changing and with more video being captured every day than before this thesis looked into motion detection through frame differencing. More specifically, this thesis examined what effect small camera movements had to the motion detection and if there was an algorithm that could mitigate some of the unwanted camera movements from the resulting motion detection.
This was examined through having different test case with different amount of inflicted movement of the camera. The test cases with and without moving objects in the frame were examined. The moving objects were annotated and used as a metric to determine the F1-score. The two algorithms that were compared both managed to mitigate some vibrations and movement for some threshold values.
If more time would have been available more annotated with higher precision (pixel perfect) would have been helpful. Potential future research could be a heuristic-based approach or if a deep neural network can perform frame differencing and mitigate small camera movements.