Video Segmentation of Illuminance Abrupt Variation Based on MOGs and Interframe Gradient Cross-correlation

Moving object segmentation in video sequence is the foundation of motion analysis and motion track. In this paper, a novel segmentation algorithm is proposed to solve illuminance abrupt change problem, which is based on MOGs and interframe gradient cross-correlation. Firstly, a primary foreground segmentation is obtained, where an adaptive MOGs (mixture of Gaussians) is established for each pixel´s luminance. Secondly, luminance and chroma of each pixel are varying in a big scale followed abrupt illuminance change, which cause mismatch between a pixel´s luminance and its MOGs, and misclassify a vast of background pixels as foreground. To adapt illuminance sudden variation, an improved method combining interframe gradient information is adopted to correct the initial segmentation. Finally, morphological methods are used to remove shadows and isolated noise pixels. Extensive experiments are performed with various video sequences, which prove that this method is robust and of high segmentation accuracy