Han Bohyung, Comaniciu Dorin, Zhu Ying, Davis Larry S
Advance Project Center, Mobileye Vision Technologies, Princeton, NJ 08542, USA.
IEEE Trans Pattern Anal Mach Intell. 2008 Jul;30(7):1186-97. doi: 10.1109/TPAMI.2007.70771.
Visual features are commonly modeled with probability density functions in computer vision problems, but current methods such as a mixture of Gaussians and kernel density estimation suffer from either the lack of flexibility, by fixing or limiting the number of Gaussian components in the mixture, or large memory requirement, by maintaining a non-parametric representation of the density. These problems are aggravated in real-time computer vision applications since density functions are required to be updated as new data becomes available. We present a novel kernel density approximation technique based on the mean-shift mode finding algorithm, and describe an efficient method to sequentially propagate the density modes over time. While the proposed density representation is memory efficient, which is typical for mixture densities, it inherits the flexibility of non-parametric methods by allowing the number of components to be variable. The accuracy and compactness of the sequential kernel density approximation technique is illustrated by both simulations and experiments. Sequential kernel density approximation is applied to on-line target appearance modeling for visual tracking, and its performance is demonstrated on a variety of videos.
在计算机视觉问题中，视觉特征通常用概率密度函数来建模，但当前的方法，如高斯混合模型和核密度估计，要么由于固定或限制混合中高斯分量的数量而缺乏灵活性，要么由于维护密度的非参数表示而需要大量内存。在实时计算机视觉应用中，这些问题会更加严重，因为随着新数据的出现，密度函数需要更新。我们提出了一种基于均值漂移模式查找算法的新型核密度近似技术，并描述了一种随时间顺序传播密度模式的有效方法。虽然所提出的密度表示在内存方面很高效，这是混合密度的典型特征，但它通过允许分量数量可变而继承了非参数方法的灵活性。通过模拟和实验说明了顺序核密度近似技术的准确性和紧凑性。顺序核密度近似应用于视觉跟踪的在线目标外观建模，并在各种视频上展示了其性能。
