The Caltech Fish Counting Dataset: A Benchmark for Multiple-Object Tracking and Counting
It provides a benchmark for researchers to tackle domain generalization in MOT and counting, specifically for natural-world applications like fish monitoring, but is incremental as it focuses on a new dataset rather than novel methods.
The paper introduces the Caltech Fish Counting Dataset (CFC), a large-scale dataset with over 500,000 annotations in 1,500 sonar videos, to address detection, tracking, and counting of fish in low signal-to-noise conditions, highlighting challenges in domain generalization for multiple-object tracking and counting.
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.