Tracking Small Birds by Detection Candidate Region Filtering and Detection History-aware Association
本论文重点关注在全景视频中出现的小鸟的跟踪。当跟踪对象的尺寸在图像中很小（小对象跟踪）且移动迅速时，目标检测和关联会受到损害。为解决这些问题，我们提出了自适应切片辅助高强度交互（Adaptive SAHI）和检测历史感知相似度标准（DHSC）来减少检测应用的候选区域数量，并准确地将对象在连续帧中关联起来。在NUBird2022数据集上的实验证实了所提出方法的有效性，通过提高准确性和速度来证明了其有效性。
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.
https://arxiv.org/abs/2405.17323
https://arxiv.org/pdf/2405.17323.pdf