针对基于可见光和红外图像的分析型融合跟踪算法在复杂环境下的鲁棒性不高,提出一种新颖的基于优化协同训练理论的自适应分析型融合跟踪算法.首先,通过加权多示例学习boosting技术分别从基于可见光和红外图像的弱分类器池中实现判别能力最好的弱分类器挑选,减弱引入的误差样本对联合分类器判别能力的影响；然后,在自适应先验知识引入机制辅助下,完成分类器样本包的协同训练更新,减小相互引入误差样本的概率；最后,通过误差模型完成算法有效性分析.多组序列跟踪的对比实验结果展示了该算法各部分对提高跟踪鲁棒性的贡献,验证了该算法相比于基于单源图像或其它融合机制的跟踪算法更好的鲁棒性.

As analytical fusion tracking algorithms based on visible and infrared images always have low robustness in complex environment, a novel adaptive analytical fusion tracking algorithm based on optimized co-training framework was proposed. Firstly, selecting the most discriminative weak classifiers from weak classifier pools based on infrared and visible images respectively are achieved by weighted multiple instance learning boosting technology, which relieving classifiers’ discriminative capacity decreasing owing to the added error positive samples. Then, classifiers’ sample bags are updated by co-training criterion under the help of adaptive prior knowledge import strategy. Lastly, efficiency analysis of the proposed algorithm was achieved based on error model. Comparative experiments on multiple sequences tracking show the contributions for improving tracking robustness from different parts of the proposed algorithm, and then, demonstrate that it outperforms state-of-the-art tracking algorithms based on single source image or other fusion schemes on robustness.