Guo Wenhua, Feng Zuren, Ren Xiaodong
Systems Engineering Institute, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
Sensors (Basel). 2017 Mar 31;17(4):739. doi: 10.3390/s17040739.
Object tracking has remained a challenging problem in recent years. Most of the trackers can not work well, especially when dealing with problems such as similarly colored backgrounds, object occlusions, low illumination, or sudden illumination changes in real scenes. A centroid iteration algorithm using multiple features and a posterior probability criterion is presented to solve these problems. The model representation of the object and the similarity measure are two key factors that greatly influence the performance of the tracker. Firstly, this paper propose using a local texture feature which is a generalization of the local binary pattern (LBP) descriptor, which we call the double center-symmetric local binary pattern (DCS-LBP). This feature shows great discrimination between similar regions and high robustness to noise. By analyzing DCS-LBP patterns, a simplified DCS-LBP is used to improve the object texture model called the SDCS-LBP. The SDCS-LBP is able to describe the primitive structural information of the local image such as edges and corners. Then, the SDCS-LBP and the color are combined to generate the multiple features as the target model. Secondly, a posterior probability measure is introduced to reduce the rate of matching mistakes. Three strategies of target model update are employed. Experimental results show that our proposed algorithm is effective in improving tracking performance in complicated real scenarios compared with some state-of-the-art methods.
近年来，目标跟踪一直是一个具有挑战性的问题。大多数跟踪器都不能很好地工作，尤其是在处理诸如真实场景中颜色相似的背景、目标遮挡、低光照或光照突然变化等问题时。提出了一种使用多种特征和后验概率准则的质心迭代算法来解决这些问题。目标的模型表示和相似性度量是极大影响跟踪器性能的两个关键因素。首先，本文提出使用一种局部纹理特征，它是局部二值模式（LBP）描述符的推广，我们称之为双中心对称局部二值模式（DCS-LBP）。该特征在相似区域之间表现出很强的区分能力，并且对噪声具有很高的鲁棒性。通过分析DCS-LBP模式，使用一种简化的DCS-LBP来改进目标纹理模型，即SDCS-LBP。SDCS-LBP能够描述局部图像的基本结构信息，如边缘和角点。然后，将SDCS-LBP与颜色相结合，生成多种特征作为目标模型。其次，引入后验概率度量以降低匹配错误率。采用了三种目标模型更新策略。实验结果表明，与一些当前最先进的方法相比，我们提出的算法在复杂真实场景中有效提高了跟踪性能。
