National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, No. 95, Zhongguancun East Road, PO Box 2728, Beijing 100190, PR China.
IEEE Trans Pattern Anal Mach Intell. 2012 Dec;34(12):2420-40. doi: 10.1109/TPAMI.2012.42.
Object appearance modeling is crucial for tracking objects, especially in videos captured by nonstationary cameras and for reasoning about occlusions between multiple moving objects. Based on the log-euclidean Riemannian metric on symmetric positive definite matrices, we propose an incremental log-euclidean Riemannian subspace learning algorithm in which covariance matrices of image features are mapped into a vector space with the log-euclidean Riemannian metric. Based on the subspace learning algorithm, we develop a log-euclidean block-division appearance model which captures both the global and local spatial layout information about object appearances. Single object tracking and multi-object tracking with occlusion reasoning are then achieved by particle filtering-based Bayesian state inference. During tracking, incremental updating of the log-euclidean block-division appearance model captures changes in object appearance. For multi-object tracking, the appearance models of the objects can be updated even in the presence of occlusions. Experimental results demonstrate that the proposed tracking algorithm obtains more accurate results than six state-of-the-art tracking algorithms.
目标外观建模对于目标跟踪至关重要，尤其是在非静止相机拍摄的视频中，以及在多个移动物体之间的遮挡推理方面。基于对称正定矩阵上的对数欧几里得黎曼度量，我们提出了一种增量对数欧几里得黎曼子空间学习算法，其中图像特征的协方差矩阵被映射到具有对数欧几里得黎曼度量的向量空间中。基于子空间学习算法，我们开发了一种对数欧几里得块划分外观模型，该模型可以捕获关于目标外观的全局和局部空间布局信息。然后，通过基于粒子滤波的贝叶斯状态推断来实现单目标跟踪和具有遮挡推理的多目标跟踪。在跟踪过程中，对数欧几里得块划分外观模型的增量更新可以捕获目标外观的变化。对于多目标跟踪，即使存在遮挡，也可以更新物体的外观模型。实验结果表明，所提出的跟踪算法比六种最先进的跟踪算法获得了更准确的结果。
