Scalable-Width Temporal Edge Detection for Recursive Background Recovery in adaptive background modeling

Background modeling is a preliminary processing task that generates background or reference frame for moving object detection. Apart from tracking background scene, a good quality background model will prevent false detection. Fuzzy Running Average (FRA) is an efficient background modeling scheme which employs a Fuzzy Inference System (FIS). Its high selectivity in background update prevents foreground object from appearing in the reference frame. Later, Extended Fuzzy Running Average (EFRA) was developed to allow FRA to recover the occlusion if a background object starts moving. However, the recovery rate of EFRA is limited due to the use of fixed width for detecting the occlusion's edge. In this paper, a newly developed method based on Scalable-Width Temporal Edge Detection (SWTED) is proposed to enhance the EFRA performance in locating and recovering the occlusion with higher rate. The results obtained show that the improved EFRA significantly outperforms FRA in background tracking. The algorithm is also well suited for real-time implementation.

Figure optionsDownload as PowerPoint slideHighlights
► Performance enhancement for a fuzzy-based background model on recovery rate.
► Occlusion's edge detection by comparing registered and predicted backgrounds.
► Recursive recovery removes occlusion starting from the boundary.
► Increasing width for edge detection will increase the recovery rate.
► False object detection time is shorter with faster recovery rate.