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首页> 外文会议>European Conference on Computer Vision(ECCV 2004) pt.4; 20040511-20040514; Prague; CZ >A Bayesian Framework for Multi-cue 3D Object Tracking
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A Bayesian Framework for Multi-cue 3D Object Tracking

机译：用于多线索3D对象跟踪的贝叶斯框架

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摘要

This paper presents a Bayesian framework for multi-cue 3D object tracking of deformable objects. The proposed spatio-temporal object representation involves a set of distinct linear subspace models or Dynamic Point Distribution Models (DPDMs), which can deal with both continuous and discontinuous appearance changes; the representation is learned fully automatically from training data. The representation is enriched with texture information by means of intensity histograms, which are compared using the Bhattacharyya coefficient. Direct 3D measurement is furthermore provided by a stereo system. State propagation is achieved by a particle filter which combines the three cues shape, texture and depth, in its observation density function. The tracking framework integrates an independently operating object detection system by means of importance sampling. We illustrate the benefit of our integrated multi-cue tracking approach on pedestrian tracking from a moving vehicle.
机译：本文提出了用于可变形对象的多线索3D对象跟踪的贝叶斯框架。所提出的时空对象表示涉及一组不同的线性子空间模型或动态点分布模型（DPDM），它们可以处理连续和不连续的外观变化。表示是从训练数据中完全自动学习的。通过强度直方图丰富了纹理信息，并使用Bhattacharyya系数对其进行了比较。立体系统还提供直接3D测量。通过粒子滤波器实现状态传播，该粒子滤波器在其观察密度函数中结合了三个线索的形状，纹理和深度。跟踪框架通过重要性采样集成了独立运行的对象检测系统。我们说明了集成的多线索跟踪方法对从行驶中的车辆进行行人跟踪的好处。

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