Single Object Tracking in LiDAR point cloud is one of the most essential parts of environmental perception, in which small objects are inevitable in real-world scenarios and will bring a significant barrier to the accurate location. However, the existing methods concentrate more on exploring universal architectures for common categories and overlook the challenges that small objects have long been thorny due to the relative deficiency of foreground points and a low tolerance for disturbances. To this end, we propose a Siamese network-based method for small object tracking in the LiDAR point cloud, which is composed of the target-awareness prototype mining (TAPM) module and the regional grid subdivision (RGS) module. The TAPM module adopts the reconstruction mechanism of the masked decoder to learn the prototype in the feature space, aiming to highlight the presence of foreground points that will facilitate the subsequent location of small objects. Through the above prototype is capable of accentuating the small object of interest, the positioning deviation in feature maps still leads to high tracking errors. To alleviate this issue, the RGS module is proposed to recover the fine-grained features of the search region based on ViT and pixel shuffle layers. In addition, apart from the normal settings, we elaborately design a scaling experiment to evaluate the robustness of the different trackers on small objects. Extensive experiments on KITTI and nuScenes demonstrate that our method can effectively improve the tracking performance of small targets without affecting normal-sized objects.

翻译：单目标跟踪是激光雷达点云环境感知中最关键的环节之一，真实场景中不可避免地存在小目标，这将给精确定位带来显著障碍。然而，现有方法更侧重于探索通用类别的通用架构，忽视了小目标因前景点相对稀疏且对干扰容忍度低而长期存在的挑战。为此，我们提出一种基于孪生网络的激光雷达点云小目标跟踪方法，该方法由目标感知原型挖掘模块和区域网格细分模块组成。目标感知原型挖掘模块采用掩码解码器的重建机制学习特征空间中的原型，旨在突出前景点的存在，从而促进后续对小目标的定位。尽管上述原型能够强化感兴趣的小目标，但特征图中的定位偏差仍会导致较高的跟踪误差。为解决该问题，我们提出区域网格细分模块，基于ViT和像素重组层恢复搜索区域的细粒度特征。此外，除常规设置外，我们精心设计了缩放实验以评估不同跟踪器对小目标的鲁棒性。在KITTI和nuScenes数据集上的大量实验表明，所提方法能在不影响常规尺寸目标的前提下有效提升小目标的跟踪性能。