面向个体人员特征的跨模态目标跟踪算法

Cross-modal object tracking algorithm based on pedestrian attribute

针对类内干扰影响基于个体人员特征目标跟踪算法的精确性和鲁棒性问题,分析当前跟踪算法在个体人员跟踪方面存在的不足,提出了利用语言先验知识引导辅助跟踪器的方法。在视觉跟踪器的基础上增加语言引导分支,对跟踪目标产生注意力,从而减少对类内干扰的影响。利用位置置信度进行回归目标框定位的方法解决基于孪生网络目标跟踪算法中利用分类置信度定位候选目标框的局限性,实现跨模态信息融合提升特定目标跟踪的精度。为提升所提模型对特定人员目标跟踪的针对性,构建了跨模态的人员目标跟踪数据集用于训练和验证。实验表明:所提模型应用于个体人员跟踪时表现更佳,其有效性得到了证明。

The accuracy and robustness of the object tracking algorithm have been influenced by the intra-class interference when tracking pedestrian.In this paper,we analyze the drawbacks of current tracking algorithms and propose a model to combine the visual feature and language priori to improve the performance of the tracker.The language guided branch is added to supervise the visual tracking branch by generating the attention,so the intra-class interference can be alleviated.We also propose a method to improve the accuracy of thecross-modal object tracking based on the location confidence instead of classification confidence for siamese trackers.To validate our method,we customize the dataset specialized for pedestrian tracking.The experiment shows the effectiveness of this model.