This paper focuses on tracking birds that appear small in a panoramic video.
When the size of the tracked object is small in the image (small object
tracking) and move quickly, object detection and association suffers. To
address these problems, we propose Adaptive Slicing Aided Hyper Inference
(Adaptive SAHI), which reduces the candidate regions to apply detection, and
Detection History-aware Similarity Criterion (DHSC), which accurately
associates objects in consecutive frames based on the detection history.
Experiments on the NUBird2022 dataset verifies the effectiveness of the
proposed method by showing improvements in both accuracy and speed.

该论文研究了在全景视频中出现的小鸟的跟踪问题，提出了一种自适应切片辅助高超推理方法（Adaptive SAHI），用于减少候选区域并改善检测，以及一种基于检测历史的相似性判据（DHSC），用于准确关联连续帧中的对象。在 NUBird2022 数据集上的实验证实了该方法在准确性和速度方面的改进。