基于高阶时空模型的视觉传感网络数据关联方法

Data Association in Visual Sensor Networks Based on High-order Spatio-temporal Model

数据关联是视觉传感网络联合监控系统的基本问题之一.本文针对存在漏检条件下视觉传感网络的数据关联问题,提出高阶时空观测模型并在此基础上建立了数据关联问题的动态贝叶斯网络描述.给出了数据关联精确推理算法并分析了其计算复杂性,接着根据不同的独立性假设提出两种近似推理算法以降低算法运算量,并将提出的推理算法嵌入到EM算法框架中,使该算法能够应用于目标外观模型未知的情况.仿真和实验结果表明了所提方法的有效性.

One of the fundamental requirements for visual surveillance with visual sensor networks is the correct association of camera s observations with the tracks of objects under tracking. In this paper, we propose a high-order spatio-temporal model to deal with the problem of missing detection, and then formulate the data association problem with dynamic Bayesian networks. After presenting the exact inference algorithm for data association and showing its computational intractability, we derive two approximate inference algorithms based on different independency assumptions. To apply the algorithms when the object appearance model is unavailable, we incorporate the proposed inference algorithms into EM framework. Simulation and experimental results demonstrate the effectiveness of the proposed method.