摄像机间目标关联是无重叠视域多摄像机目标持续跟踪的关键.提出了一种只利用人体目标外观，完全不依赖于空时关系的人体目标再识别算法，利用识别结果直接进行跨摄像机间人体目标关联，而不依赖于目标的捕获时间和路径限制.对跟踪视频前景图像序列提取互补性视觉单词树直方图和全局颜色直方图二种特征，采用支持向量机增量学习在线训练二种特征的人体外观辨别模型，再利用多类线性规划增强算法对二种特征的支持向量机模型进行在线自适应融合.实验结果表明，本文算法具有较强的在线学习能力，能增量式表达人体目标辨别性外观模型，特征融合后的模型区别性更强，有效地降低多方面条件变化的影响，获得了高识别率，且能够实现快速实时实现，相对于现有方法有了明显提升.

Object association between the cameras is a key of persistent object tracking in non-overlapping multi-cameras. A people re-identification algorithm was proposed only using people appearance completely independent on the space-time relations, object association across disjoint views was carried out by directly utilizing identification result, and this method does not depend on captured time of object and path restrictions. Complementary visual word tree histogram and global color histogram were extracted from the video image sequence, and Support Vector Machine(SVM) incremental learning was used to train online distinguishing people appearance models of two features. Finally, multi-class Linear Programming Boosting (LPBoost) algorithm was introduced into on-line adaptive fusion of two SVM models. The proposed method has strong online learning ability, and can incrementally represent discriminative people appearance model. The model after fusing two features is more discriminative and effectively reduces the influence of changes in various conditions. Experimental results show that the proposed method achieves high identification rate and rapid real-time implementation which are markedly improved compared to the existent methods.