全局搜索和多实例判别特征的长时跟踪方法

Long-term Tracking Method with Global Search and Multiple Instance Discriminative Features

长时目标跟踪相对于短时目标跟踪仍然是一个巨大的挑战.然而现有的长时跟踪算法通常在面对目标频繁出现消失、目标外观发生剧变等挑战中表现不佳.本文提出了一种基于局部搜索模块和全局搜索跟踪模块的全新、鲁棒且实时的长时跟踪框架.局部搜索模块利用TransT短时跟踪器生成一系列候选框,并通过置信度评分确定最佳候选框.针对全局重新检测开发了一个新颖的全局搜索跟踪模块,以Faster R-CNN为基础模型,在RPN阶段与R-CNN阶段引入非局部操作和多级实例特征融合模块,以充分挖掘目标实例级特征.为了改进全局搜索跟踪模块的性能,设计了双模板更新策略来提升跟踪器的鲁棒能力.通过使用不同时间点上更新的模板能够更好地适应目标的变化.根据局部或全局置信度分数判断目标是否存在,并在下一帧中选择局部或全局搜索跟踪策略.同时能够为局部搜索模块估计目标的位置和大小.此外还为全局搜索跟踪器引入了排名损失函数,隐式学习了区域提议与原始查询目标的相似度.通过在多个跟踪数据集上进行大量实验对提出的跟踪框架进行了广泛评估.结果一致表明,本文提出的跟踪框架实现了令人满意的性能.

Long-term object tracking remains a formidable challenge compared to short-term object tracking.However,existing long-term tracking algorithms often perform poorly when faced with challenges such as targets frequently appearing and disappearing,and drastic changes in target appearance.This study proposes a novel,robust,and real-time long-term tracking framework based on local search modules and global search tracking modules.The local search module utilizes the TransT short-term tracker to generate a series of candidate boxes,and the best candidate box is determined through confidence scoring.A novel global search tracking module is developed for global re-detection,based on the Faster R-CNN model,with the introduction of Non-Local operations and multi-level instance feature fusion modules in the RPN and R-CNN stages,aiming to fully exploit target instance-level features.To improve the performance of the global search tracking module,a dual-template update strategy is designed to enhance the robustness of the tracker.By utilizing templates updated at different time points,the tracker can better adapt to target changes.The target presence is determined based on local or global confidence scores,and the local or global search tracking strategy is selected in the next frame.Additionally,the local search module is capable of estimating the position and size of the target.Moreover,a ranking loss function is introduced for the global search tracker,implicitly learning the similarity between region proposals and the original query target.A large number of experiments are conducted on multiple tracking datasets to comprehensively assess the proposed tracking framework.The results consistently demonstrate that the proposed tracking framework achieves satisfactory performance.