Lowering False Alarm rates in Motion Detection Scenarios using Machine Learning

Camera motion detection is a form of intruder detection that may cause high false alarm rates, especially in home environments where movements from example pets and windows may be the cause. This article explores the subject of reducing the frequency of such false alarms by applying machine learning techniques, for the specific scenario where only data regarding the motion detected is available, instead of the full image. This article introduces two competitive unsupervised learning algorithms, the first a vector quantization algorithm for filtering false alarms from window sources, the second a self-organizing map for filtering out smaller events such as pets by way of scaling based on the distance to the camera. Initial results show that the two algorithms can provide the functionality needed, but that the algorithms need to be more robust to be used well in an unsupervised live situation. The majority of the results have been obtained using simulated data rather than live data due to issues with obtaining such live data at the time of the project, with live data tests to be done as future work.