旋转自适应的多特征融合多模板学习视觉跟踪算法

目标发生旋转及遇到外界干扰时会给目标跟踪带来巨大挑战,针对该问题,文中提出旋转自适应的多特征融合多模板学习跟踪算法.首先,构建具有互补特性的多模板学习模型,全局滤波器模板用于跟踪目标,当判定滤波器模板确定全局滤波器模板被污染时,使用修正滤波器模板对全局滤波器模板进行修正.然后,将颜色直方图作为视觉补充信息和VGGNet-19特征图进行自适应融合,提升全局滤波器模板对目标外观的判别能力.最后,提出旋转自适应策略,采用改进的跟踪置信度,估计跟踪框最佳旋转角度,减轻目标旋转带来的全局滤波器模板性能衰退.在OTB-2013、OTB-2015数据集上的实验表明,文中算法的成功率和精确率较高.

Visual Tracking Algorithm Based on Rotation Adaptation,Multi-feature Fusion and Multi-template Learning

Visual target tracking remains a hard problem due to unpredictable target rotation and external interference. To address this issue, a target tracking algorithm based on rotation adaptation, multi-feature fusion and multi-template learning(RA-MFML) is proposed. Firstly, a multi-template learning model with complementary characteristics is constructed. The global filter template is used for tracking the target. When the global filter template is determined to be contaminated by the decidable filter template, it is corrected by the modified filter template. Then, the color histogram is regarded as visual supplementary information and fused with feature map of VGGNet-19 adaptively. The discriminating ability of the global filter template for object appearance is thus improved. Finally, a rotation adaptation strategy is proposed. The improved tracking confidence is utilized for the estimation of the optimal rotation angle of the tracking box to alleviate performance degradation of the global filter template caused by target rotation. The experiment on OTB-2013 and OTB-2015 datasets demonstrate that RA-MFML is superior in success rate and precision.