桥接自动化的喙鲸追踪中的回声定位间隙
Bridging Echolocation Gaps in Automated Beaked Whale Tracking
- Scripps Institution of Oceanography（斯克里普斯海洋研究所）
- University of California at San Diego（加利福尼亚大学圣迭戈分校）
- Institute of Telecommunications, TU Wien（维也纳工业大学电信研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一种结合信念传播MTT与轨迹平滑拼接的多阶段目标估计方法，以桥接喙鲸回声定位点击序列中的大间隙，减少轨迹碎片化，并在GOSPA指标上优于现有方法。
AI中文摘要：
被动声学监测（PAM）是一种有效且广泛使用的工具，用于追踪海洋哺乳动物，特别是喙鲸，这些鲸鱼由于深潜行为而很少被视觉观察到。然而，PAM方法产生的大量数据集通常需要耗时的手动标记来识别记录音频中的鲸鱼轨迹。自动化多目标跟踪（MTT）方法可以显著减少人工工作量，但当前方法由于喙鲸回声定位点击的不规则性而难以形成连续轨迹。更准确地说，规则的点击序列经常被较长的停顿打断，这些停顿发生在鲸鱼背对传感器或停止点击时。因此，检测概率难以准确建模，MTT轨迹在这些停顿处变得碎片化。在本文中，我们提出了一种多阶段目标估计方法，旨在通过将基于信念传播的MTT与轨迹平滑和拼接相结合，来桥接点击序列中的大间隙。我们使用鹅喙鲸（Ziphius cavirostris）的点击声学记录验证了我们的方法，并证明在连续漏检的情况下，它改善了轨迹估计并减少了碎片化。当使用广义最优子模式分配（GOSPA）指标评估时，我们的方法通过减少漏检目标误差而优于现有的MTT参考方法。
英文摘要：
Passive acoustic monitoring (PAM) is an effective and widely used tool for tracking marine mammals, particularly beaked whales, which are infrequently observed visually because of their deep-diving behavior. However, the large data sets generated by PAM methods often require time-consuming hand labeling to identify whale trajectories in the recorded audio. Automated multi-target tracking (MTT) methods could significantly reduce human workload, but current methods have difficulty forming continuous tracks because of the irregularity of beaked whale echolocation clicks. More precisely, regular sequences of clicks are often interrupted by longer pauses that occur when whales face away from the sensors or stop clicking. Consequently, the probability of detection is difficult to model accurately, and MTT trajectories become fragmented at these pauses. In this paper, we propose a multistage target-estimation method aimed at bridging large gaps in click sequences by combining belief propagation-based MTT with track smoothing and stitching. We validate our method using acoustic recordings of clicks from goose-beaked whales (Ziphius cavirostris), and demonstrate that it improves track estimates and reduces fragmentation in the presence of consecutive missed detections. When evaluated with the generalized optimal subpattern assignment (GOSPA) metric, our method outperforms existing MTT reference methods through reductions in missed-target errors.
