Efficient visual object detection with spatially global Gaussian mixture models and uncertainties

• We developed a spatial global Gaussian mixture model for background segmentation.
• We included pixel displacement uncertainties to improve accuracy in dynamic scenes.
• We tested the SGGMM without and with uncertainty modelling in different scenes.
• We implemented and validated our approach in an embedded camera sensor network node.

In this paper, we deal with the problem of visual detection of moving objects using innovative Gaussian mixture models (GMM). The proposed method, the Spatially Global Gaussian Mixture Model (SGGMM) uses RGB and pixel uncertainty for background modelling. The SGGMM with colours only is used for scenes with moderate illumination changes. When combined with pixel uncertainty statistics, the method can deal efficiently with dynamic backgrounds and scene backgrounds with fast change in luminosity. Experimental evaluation in indoor and outdoor environments shows the performance of the foreground segmentation with the proposed SGGMM model using solely RGB colour or in combination with pixel uncertainties. These experimental scenarios take into account changes in the background within the scene. They are also used to compare the proposed technique with other state-of-the-art segmentation approaches in terms of accuracy and execution time performance. Further, our solution is implemented and tested in embedded camera sensor network nodes.