  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.
