We present the Caltech Fish Counting Dataset (CFC), a large-scale dataset for detecting, tracking, and counting fish in sonar videos. We identify sonar videos as a rich source of data for advancing low signal-to-noise computer vision applications and tackling domain generalization in multiple-object tracking (MOT) and counting. In comparison to existing MOT and counting datasets, which are largely restricted to videos of people and vehicles in cities, CFC is sourced from a natural-world domain where targets are not easily resolvable and appearance features cannot be easily leveraged for target re-identification. With over half a million annotations in over 1,500 videos sourced from seven different sonar cameras, CFC allows researchers to train MOT and counting algorithms and evaluate generalization performance at unseen test locations. We perform extensive baseline experiments and identify key challenges and opportunities for advancing the state of the art in generalization in MOT and counting.

本文介绍了Caltech鱼类计数数据集（CFC），它是一种大规模的数据集，用于检测、跟踪和计数声纳视频中的鱼类。该数据集为低信噪比计算机视觉应用程序提供了丰富的数据来源，并且比现有的大多数计数数据集来源更自然。我们使用基线实验来评估MOT和计数中的泛化表现，并确定了进一步发展状态的关键挑战和机遇。

Caltech鱼类计数数据集：多目标跟踪和计数的基准测试