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.

翻译：密集物体计数或人群计数得益于视觉领域的最新发展已取得长足进步。然而，旨在统计与环境背景融合的目标数量的不可辨对象计数仍具挑战。当前公开数据集中，基于图像的物体计数数据集占据主流地位。为此，我们提出一个名为YoutubeFish-35的大规模数据集，包含35个高清视频序列（高帧率）及跨越多种选定场景的超过15万个标注中心点。为建立基准测试，我们选取三种主流密集物体计数方法，并在新数据集上对其进行了系统评估。我们提出TransVidCount——一种新型强基线模型，将密度分支与回归分支在时序域统一框架中融合，能够有效解决不可辨对象计数问题，并在YoutubeFish-35数据集上达到当前最优性能。