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Online domain-shift learning and object tracking based on nonlinear dynamic models and particle filters on Riemannian manifolds

机译：基于非线性动力学模型和黎曼流形上的粒子滤波的在线域位移学习和目标跟踪

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摘要

This paper proposes a novel online domain-shift appearance learning and object tracking scheme on a Riemannian manifold for visual and infrared videos, especially for video scenarios containing large deformable objects with fast out-of-plane pose changes that could be accompanied by partial occlusions. Although Riemannian manifolds and covariance descriptors are promising for visual object tracking, the use of Riemannian mean from a window of observations, spatially insensitive covariance descriptors, fast significant out-of-plane (non-planar) pose changes, and long-term partial occlusions of large-size deformable objects in video limits the performance of such trackers. The proposed method tackles these issues with the following main contributions: (a) Proposing a Bayesian formulation on Riemannian manifolds by using particle filters on the manifold and using appearance particles in each time instant for computing the Riemannian mean, rather than using a window of observations. (b) Proposing a nonlinear dynamic model for online domain-shift learning on the manifold, where the model includes both manifold object appearance and its velocity. (c) Introducing a criterion-based partial occlusion handling approach in online learning. (d) Tracking object bounding box by using affine parametric shape modeling with manifold appearance embedded. (e) Incorporating spatial, frequency and orientation information in the covariance descriptor by extracting Gabor features in a partitioned bounding box. (f) Effectively applying to both visual-band videos and thermal-infrared videos. To realize the proposed tracker, two particle filters are employed: one is applied on the Riemannian manifold for generating candidate appearance particles and another is on vector space for generating candidate box particles. Further, tracking and online learning are performed in alternation to mitigate the tracking drift. Experiments on both visual and infrared videos have shown robust tracking performance of the proposed scheme. Comparisons and evaluations with ten existing state-of-art trackers provide further support to the proposed scheme.
机译：本文针对视觉和红外视频提出了一种基于黎曼流形的新型在线域移位外观学习和对象跟踪方案，特别是对于包含大型可变形对象，面外姿态快速变化且可能伴随部分遮挡的视频场景。尽管黎曼流形和协方差描述符有望用于视觉对象跟踪，但可以使用观察窗中的黎曼平均值，空间不敏感的协方差描述符，快速有效的平面外（非平面）姿势变化以及长期局部遮挡视频中大型可变形对象的数量限制了此类跟踪器的性能。所提出的方法通过以下主要贡献解决了这些问题：（a）通过在流形上使用粒子过滤器并在每个瞬时使用外观粒子来计算黎曼平均值，而不是使用观察窗，在黎曼流形上提出贝叶斯公式。 （b）提出用于流形上在线域移位学习的非线性动力学模型，其中该模型包括流形对象外观及其速度。 （c）在在线学习中引入基于标准的部分遮挡处理方法。 （d）通过使用仿射参量形状建模并嵌入流形外观来跟踪对象边界框。 （e）通过在分区边界框中提取Gabor特征，将空间，频率和方向信息纳入协方差描述符。 （f）有效地适用于可视带视频和热红外视频。为了实现所提出的跟踪器，使用了两个粒子过滤器：一个应用于黎曼流形以生成候选外观粒子，另一个应用于向量空间以生成候选箱形粒子。此外，交替进行跟踪和在线学习以减轻跟踪漂移。视觉和红外视频实验均显示了该方案的鲁棒跟踪性能。与十个现有的最新跟踪器的比较和评估为提议的方案提供了进一步的支持。

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