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首页> 外文会议>4th International Conference on New Trends in Information Science and Service Science >Robust online tracking using orientation and color incorporated adaptive models in particle filter
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Robust online tracking using orientation and color incorporated adaptive models in particle filter

机译：在粒子滤波器中使用方向和颜色合并的自适应模型进行可靠的在线跟踪

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摘要

Moving object tracking has received much interest in the field of computer vision due to the increasing need for automated video analysis. Particles Filter is a very promising object tracking method since it is suitable for non-linear and/or non-Gaussian applications. Most particle filter applies color information in target model which might fail in the presence of similar colored objects in the scene. This paper presents the integration of color and orientation features in particle filter to make full use of the distinctive target features. Also, an improved Gaussian weighting function for target models and an updating scheme with an adaptive updating ratio are proposed. The proposed approaches are applied in the real-time video sequences with different occlusion conditions to test the robustness of the proposal. Experiment results show that stable and accurate tracking performance is achieved even when the target is occluded by a similar colored object.
机译：由于对自动视频分析的需求不断增加，运动对象跟踪已在计算机视觉领域引起了广泛兴趣。粒子滤波是一种非常有前途的目标跟踪方法，因为它适用于非线性和/或非高斯应用。大多数粒子滤镜会在目标模型中应用颜色信息，如果场景中存在相似的彩色对象，这些信息可能会失败。本文介绍了粒子过滤器中颜色和方向特征的集成，以充分利用独特的目标特征。此外，提出了一种改进的目标模型高斯加权函数和具有自适应更新率的更新方案。将所提出的方法应用于具有不同遮挡条件的实时视频序列中，以测试该提议的鲁棒性。实验结果表明，即使目标被相似的有色物体遮挡，跟踪性能也能稳定，准确地实现。

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