基于时空视频块的背景建模

提出了一种基于时空视频块的背景建模方法,时空视频块同时包含空间表观信息和时间运动信息.一个给定的背景位置在所有可能光照条件下的时空视频块集中位于一个低维的背景子空间中,而运动前景的时空视频块散布在背景子空间外的整个高维视频块空间中,采用一种高效的在线子空间学习算法实时更新背景子空间的主成分,根据时空视频块到背景子空间的距离来区分背景时空视频块和前景时空视频块.实验结果显示,本文中提出的方法能够在光照剧烈变化、前景与背景对比度较低的情况下准确地检测出前景目标.

Background Modeling Based on Spatio-Temporal Patch

A novel spatio-temporal patch based background modeling (STPBM) approach was proposed. Spatio-temporal patches (bricks) are utilized to characterize both the appearance and motion information of objects in videos. It was observed that, under all possible illumination conditions, all the bricks at a given background position lie in a low dimensional background subspace. In contrast, bricks with moving foreground are uniformly distributed in original space. Then an efficient online subspace learning method for capturing the background subspace was presented, and the incoming bricks with moving foreground could be detected according to their distance to the background subspace. Experimental results demonstrate that, compared with traditional pixel-wise or block-wise methods, our approach is more insensitive to drastic illumination changes and capable of detecting dim foreground objects under low contrast.