Robust motion detection by fusion of 6D feature space decompositions

A motion detection framework is presented that integrates spatial and temporal data by decomposition of the feature space. Temporal data is employed to monitor frame changes on a per-pixel basis, whilst the spatial data is used to provide region-based segmentation of image frames. The thresholded difference image from the change detection is then fused with the segmented image. It is found that this framework greatly improves robustness to noise, while retaining and, in some cases, improving the sensitivity to regions of interest. Further, the decomposition reduces the time required to run due to the reduction in computational complexity.