• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on >Bayesian online learning on Riemannian manifolds using a dual model with applications to video object tracking
【24h】

Bayesian online learning on Riemannian manifolds using a dual model with applications to video object tracking

机译：使用对偶模型的黎曼流形上的贝叶斯在线学习及其在视频对象跟踪中的应用

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This paper proposes a new Bayesian online learning method on a Riemannian manifold for video objects. The basic idea is to consider the dynamic appearance of an object as a point moving on a manifold, where a dual model is applied to estimate the posterior trajectory of this moving point at each time instant under the Bayesian framework. The dual model uses two state variables for modeling the online learning process on Riemannian manifolds: one is for object appearances on Riemannian manifolds, another is for velocity vectors in tangent planes of manifolds. The key difference of our method as compared with most existing Riemannian manifold tracking methods is to compute the Riemannian mean from a set of particle manifold points at each time instant rather than using a sliding window of manifold points at different times. Next to that, we propose to use Gabor filter outputs on partitioned sub-areas of object bounding box as features, from which the covariance matrix of object appearance is formed. As an application example, the proposed online learning is employed to a Riemannian manifold object tracking scheme where tracking and online learning are performed alternatively. Experiments are performed on both visual-band videos and infrared videos, and compared with two existing manifold trackers that are most relevant. Results have shown significant improvement in terms of tracking drift, tightness and accuracy of tracked boxes especially for objects with large pose changes.
机译：本文提出了一种基于黎曼流形的视频对象贝叶斯在线学习新方法。基本思想是将对象的动态外观视为在流形上移动的一个点，在贝叶斯框架下，在每个时刻应用对偶模型来估计该移动点的后向轨迹。对偶模型使用两个状态变量来对黎曼流形上的在线学习过程进行建模：一个用于黎曼流形上的对象外观，另一个用于流形切平面中的速度矢量。与大多数现有的黎曼流形跟踪方法相比，我们方法的主要区别是在每个时刻从一组粒子流形点计算黎曼平均值，而不是在不同时间使用流形点的滑动窗口。接下来，我们建议在对象边界框的分区子区域上使用Gabor滤波器输出作为特征，从中形成对象外观的协方差矩阵。作为一个应用示例，提出的在线学习被用于黎曼流形对象跟踪方案，在该方案中跟踪和在线学习是交替执行的。实验是在可见光视频和红外视频上进行的，并与两个最相关的现有流形跟踪器进行了比较。结果表明，在跟踪漂移，紧密度和跟踪盒的准确性方面都取得了显着改善，尤其是对于姿态变化较大的对象。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号