The trade-off between accuracy and the complexity of real-time background subtraction

Background subtraction used in object detection, tracking and action recognition is a typical method that separates foreground objects from the background. These applications require accuracy and a complexity reduction technique. Some approaches have been proposed to either increase accuracy or decrease complexity. However, the trade-off between increasing accuracy and reducing the complexity of background subtraction is a big challenge. To address this issue, a background subtraction-based real-time moving object-detection approach is proposed. The key contribution in authors' proposal is to use a colour image and a novel colour-gradient blending fused image to achieve accurate background/foreground segmentation. The fused image is a combination of a gradient image and a colour image to correct illumination variations and preserve the edge information. Also, thresholds are adaptively selected based on the dynamic background behaviour to attain a more robust classification system. The proposed model based on real-time and complex videos from the CD-2012 and CD-2014 change detection data sets, and the CMD data set is evaluated. Experimental results indicate that authors' method processes around 43 frames per second and requires six bytes of memory per pixel, which is noticeably more efficient and less complex than other background subtraction methods.