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Distributed optimization for global data association in non-overlapping camera networks

机译：非重叠摄像机网络中全局数据关联的分布式优化

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摘要

One of the fundamental requirements for wide-area visual surveillance using non-overlapping camera networks is the correct association of camera's observations with the tracks of objects under tracking, which is often referred to as data association problem. Most of the current systems work in a centralized manner in that the observations, or the extracted features, on all cameras need to be transmitted to a central server where some association algorithm is running. However, distributed data association scheme, which involves only local information processing on each camera node and information exchanges between neighboring cameras, is more preferable due to a number of well known reasons such as scalability of the system, robustness against single point of failure, and efficiency in use of communication bandwidth, etc. In this paper, we formulate the data association problem as finding the optimal labeling on a factor graph model, and solve the optimization problem under the framework of Dual Decomposition, which can be implemented in a distributed manner. We show the equivalence of the problem of minimizing the energy of the factor graph and that of finding optimal partition of the set of all observations into several subsets, such that all observations belonging to each subset are believed to come from a single object. And the global solution of the dual relaxation of the energy minimization problem is guaranteed to be found by using Dual Decomposition. The effectiveness of the proposed method is demonstrated by comparison with state of the art data association algorithms on real datasets collected by the ten networked cameras mounted in our campus garden.
机译：使用不重叠的摄像机网络进行广域视觉监视的基本要求之一是将摄像机的观测值与跟踪对象的轨迹正确关联，这通常被称为数据关联问题。当前大多数系统以集中方式工作，因为需要将所有摄像机上的观测值或提取的特征传输到运行某种关联算法的中央服务器。但是，由于许多众所周知的原因（例如系统的可伸缩性，针对单点故障的鲁棒性和可扩展性），更优选仅涉及每个摄像机节点上的本地信息处理以及相邻摄像机之间的信息交换的分布式数据关联方案。在本文中，我们将数据关联问题公式化为在因子图模型上找到最佳标记，并在对偶分解框架下解决了优化问题，该问题可以以分布式方式实现。我们显示出等价于最小化因子图的能量和找到将所有观测值的集合最佳划分为几个子集的问题的等价性，这样就认为属于每个子集的所有观测值都来自单个对象。而且，通过使用双重分解，可以确保找到能量最小化问题的双重松弛的全局解决方案。通过与安装在我们校园花园中的十台网络摄像机收集的真实数据集上的最新数据关联算法进行比较，证明了该方法的有效性。

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