Department of Electrical and Computer Engineering, University of California, San Diego, 9500 Gilman Dr., La Jolla, CA 92093-0407, USA.
IEEE Trans Pattern Anal Mach Intell. 2010 Jan;32(1):171-7. doi: 10.1109/TPAMI.2009.112.
A spatiotemporal saliency algorithm based on a center-surround framework is proposed. The algorithm is inspired by biological mechanisms of motion-based perceptual grouping and extends a discriminant formulation of center-surround saliency previously proposed for static imagery. Under this formulation, the saliency of a location is equated to the power of a predefined set of features to discriminate between the visual stimuli in a center and a surround window, centered at that location. The features are spatiotemporal video patches and are modeled as dynamic textures, to achieve a principled joint characterization of the spatial and temporal components of saliency. The combination of discriminant center-surround saliency with the modeling power of dynamic textures yields a robust, versatile, and fully unsupervised spatiotemporal saliency algorithm, applicable to scenes with highly dynamic backgrounds and moving cameras. The related problem of background subtraction is treated as the complement of saliency detection, by classifying nonsalient (with respect to appearance and motion dynamics) points in the visual field as background. The algorithm is tested for background subtraction on challenging sequences, and shown to substantially outperform various state-of-the-art techniques. Quantitatively, its average error rate is almost half that of the closest competitor.
提出了一种基于中心-环绕框架的时空显著度算法。该算法受到运动感知分组的生物机制的启发，并扩展了先前为静态图像提出的用于中心环绕显著度的判别公式。在这种公式下，位置的显著度等同于预定义特征集的能力，可以区分以该位置为中心的中心和环绕窗口中的视觉刺激。特征是时空视频补丁，并建模为动态纹理，以实现显著度的空间和时间分量的有原则的联合表征。判别中心环绕显著度与动态纹理的建模能力相结合，产生了一种强大、通用且完全无需监督的时空显著度算法，适用于具有高度动态背景和移动摄像机的场景。背景减除的相关问题被视为显著度检测的补充，通过将视野中相对于外观和运动动态的非显著点分类为背景。该算法在具有挑战性的序列上进行了背景减除测试，结果表明它大大优于各种最先进的技术。定量地，其平均错误率几乎是最接近的竞争对手的一半。
