Dense object counting or crowd counting has come a long way thanks to the
recent development in the vision community. However, indiscernible object
counting, which aims to count the number of targets that are blended with
respect to their surroundings, has been a challenge. Image-based object
counting datasets have been the mainstream of the current publicly available
datasets. Therefore, we propose a large-scale dataset called YoutubeFish-35,
which contains a total of 35 sequences of high-definition videos with high
frame-per-second and more than 150,000 annotated center points across a
selected variety of scenes. For benchmarking purposes, we select three
mainstream methods for dense object counting and carefully evaluate them on the
newly collected dataset. We propose TransVidCount, a new strong baseline that
combines density and regression branches along the temporal domain in a unified
framework and can effectively tackle indiscernible object counting with
state-of-the-art performance on YoutubeFish-35 dataset.

提出了一个大规模数据集 YoutubeFish-35，在该数据集上评估了三种主流的密集物体计数方法，并提出了一种新的强基线方法 TransVidCount，该方法在 YoutubeFish-35 数据集上具有最先进的性能，可有效处理不可辨别的物体计数问题。