Spatially correlated background subtraction, based on adaptive background maintenance

Moving object detection in dynamic backgrounds remains a challenging problem. Our earlier work established that the background subtraction using the covariance matrix descriptor is robust for dynamic backgrounds. The work proposed herein extends this approach further, using just two features-Hu moment and intensity. An improved local Hu moment is proposed, where the moment calculation of a pixel, involving neighboring pixels, are used in a weighted manner to reduce the effects of background moving pixels and the accurate shape localization of moving objects simultaneously. To further counter the erratic labeling of dynamic pixels, the fact that the neighboring pixels are spatially correlated is exploited for model construction and foreground detection. An adaptive model updating rate is calculated as a function of model distance. The proposed approach models each pixel with a covariance matrix and a mean feature vector and is dynamically updated. Extensive studies are made with the proposed technique to demonstrate its effectiveness.

► An improved local moment is proposed to reduce the effects of noisy moving pixels. ► Spatial correlation of neighboring pixels is exploited to reduce erratic labeling. ► An adaptive model updating rate, as a function of model distance, is formulated. ► This work uses only two features, offering a computational advantage to [20]. ► Effectiveness of our approach is experimentally validated with various video sequences.