  Crowd-sourcing is an increasingly popular tool for image analysis in animal
ecology. Computer vision methods that can utilize crowd-sourced annotations can
help scale up analysis further. In this work we study the potential to do so on
the challenging task of fine-grained counting. As opposed to the standard crowd
counting task, fine-grained counting also involves classifying attributes of
individuals in dense crowds. We introduce a new dataset from animal ecology to
enable this study that contains 1.7M crowd-sourced annotations of 8
fine-grained classes. It is the largest available dataset for fine-grained
counting and the first to enable the study of the task with crowd-sourced
annotations. We introduce methods for generating aggregate "ground truths" from
the collected annotations, as well as a counting method that can utilize the
aggregate information. Our method improves results by 8% over a comparable
baseline, indicating the potential for algorithms to learn fine-grained
counting using crowd-sourced supervision.
