中文摘要：

目标匹配是在大范围多摄像机监控网络中进行连续目标跟踪的基础,对无重叠视野多摄像机网络中的目标匹配进行研究,提出了一种基于自适应加权二部图的多特征目标匹配算法。考虑到不同摄像机视野下成像角度、光照的差异,采用多特征融合技术构造目标的观测模型,并利用贝叶斯准则将目标匹配问题表示成最大后验概率（MAP）问题。同时,提出一种自适应加权二部图替代MAP问题,并利用Kuhn-Munkres算法解出二部图的最大权匹配。通过对监控数据进行试验,表明本文算法可在接受的时间范围内改善目标匹配的准确度。

英文摘要：

Object matching is the basis for continuous object tracking under wide area monitoring using camera network. This paper focuses on object matching across non-overlapping camera views. A multi-fea- tures object matching approach based on adaptive weighted bipartite graph is proposed. Multiple features are employed to construct an observation model due to view variation and illumination change across differ- ent camera views. Object matching is then represented as a maximum a posteriori （MAP） problem based the Bayesian rule. Meanwhile, the MAP problem is replaced using an adaptive weighted bipartite graph which is then solved by the Kuhn-Munkres algorithm. Experimental results under realistic camera network indicate that our approach can improve the accuracy of object matching across non-overlapping camera views within an acceptable time.