Department of Computer Science, National Chiao Tung University, Hsinchu 30010, Taiwan.
IEEE Trans Image Process. 2011 Mar;20(3):822-36. doi: 10.1109/TIP.2010.2075938. Epub 2010 Sep 13.
To model a scene for background subtraction, Gaussian mixture modeling (GMM) is a popular choice for its capability of adaptation to background variations. However, GMM often suffers from a tradeoff between robustness to background changes and sensitivity to foreground abnormalities and is inefficient in managing the tradeoff for various surveillance scenarios. By reviewing the formulations of GMM, we identify that such a tradeoff can be easily controlled by adaptive adjustments of the GMM's learning rates for image pixels at different locations and of distinct properties. A new rate control scheme based on high-level feedback is then developed to provide better regularization of background adaptation for GMM and to help resolving the tradeoff. Additionally, to handle lighting variations that change too fast to be caught by GMM, a heuristic rooting in frame difference is proposed to assist the proposed rate control scheme for reducing false foreground alarms. Experiments show the proposed learning rate control scheme, together with the heuristic for adaptation of over-quick lighting change, gives better performance than conventional GMM approaches.
为了对场景建模进行背景减除，高斯混合模型（Gaussian mixture modeling，GMM）因其能够适应背景变化而成为一种流行的选择。然而，GMM 通常在背景变化的鲁棒性和对前景异常的敏感性之间存在权衡，并且在管理各种监控场景的权衡方面效率低下。通过回顾 GMM 的公式，我们发现这种权衡可以通过自适应调整不同位置和不同属性的图像像素的 GMM 学习率来轻松控制。然后，开发了一种基于高层反馈的新速率控制方案，为 GMM 提供更好的背景自适应正则化，并有助于解决这种权衡。此外，为了处理 GMM 无法捕捉到的快速变化的光照变化，提出了一种基于帧差的启发式方法来辅助所提出的速率控制方案，以减少误报的前景警报。实验表明，所提出的学习率控制方案以及用于适应过快光照变化的启发式方法，比传统的 GMM 方法具有更好的性能。
