Distributed EM Learning for Appearance Based Multi-Camera Tracking
Distributed EM Learning for Appearance Based Multi-Camera Tracking
复制标题
DOI：
10.1109/icdsc.2007.4357522
复制
发表时间：
2007-09
期刊：
影响因子：
--
通讯作者：
Thomas Mensink;W. Zajdel;B. Kröse
中科院分区：
文献类型：
--
作者：
Thomas Mensink;W. Zajdel;B. Kröse
在广阔的区域（例如机场）的视觉监控依赖于观察非重叠场景的摄像机。多人跟踪需要在一个人离开一个视野，然后出现在另一个视野时重新识别他/她。为此，我们使用外观线索。在假设单个人的所有观察都是高斯分布的情况下，我们的方法中的观察模型由高斯混合组成。在本文中，我们提出了一种分布式方法来学习这种MoG，其中每个摄像机都从自己的观察和与其他摄像机的通信中进行学习。为此，我们提出了多观测新闻广播EM算法，这是最近开发的新闻广播EM的调整版本。所提出的算法进行了测试，人工生成的数据和收集的真实世界的观察系统的摄像头在办公楼。
Visual surveillance in wide areas (e.g. airports) relies on cameras that observe non-overlapping scenes. Multi-person tracking requires re-identification of a person when he/she leaves one field of view, and later appears at another. For this, we use appearance cues. Under the assumption that all observations of a single person are Gaussian distributed, the observation model in our approach consists of a Mixture of Gaussians. In this paper we propose a distributed approach for learning this MoG, where every camera learns from both its own observations and communication with other cameras. We present the multi-observations newscast EM algorithm for this, which is an adjusted version of the recently developed newscast EM. The presented algorithm is tested on artificial generated data and on a collection of real-world observations gathered by a system of cameras in an office building.