Robust Foreground Object Segmentation via Adaptive Region-Based Background Modelling

We propose a region-based foreground object segmentation method capable of dealing with image sequences containing noise, illumination variations and dynamic backgrounds (as often present in outdoor environments). The method utilises contextual spatial information through analysing each frame on an overlapping block-by-block basis and obtaining a low-dimensional texture descriptor for each block. Each descriptor is passed through an adaptive multi-stage classifier, comprised of a likelihood evaluation, an illumination invariant measure, and a temporal correlation check. The overlapping of blocks not only ensures smooth contours of the foreground objects but also effectively minimises the number of false positives in the generated foreground masks. The parameter settings are robust against wide variety of sequences and post-processing of foreground masks is not required. Experiments on the challenging I2 R dataset show that the proposed method obtains considerably better results (both ...

• Type: Journal
• Year: 2010
• Where: ICPR
• Authors: Vikas Reddy, Conrad Sanderson, Brian C. Lovell
• Keywords: Computer Vision, Foreground Masks, Generated Foreground Masks, ICPR 2010, Low-dimensional Texture Descriptor