一种用于水下视频中难以辨识目标计数的密度引导时序注意力Transformer
A Density-Guided Temporal Attention Transformer for Indiscernible Object Counting in Underwater Video
AI总结：
针对水下视频中难以辨识目标计数的难题，该研究构建了大规模水下视频数据集YoutubeFish-35，并提出结合密度与回归分支的TransVidCount模型，取得了当前最优的计数性能。
摘要：
得益于视觉领域的近期发展，密集目标计数或人群计数已取得长足进展。然而，旨在统计与周围环境融为一体的目标数量的难以辨识目标计数仍是一项挑战。当前公开数据集中，基于图像的目标计数数据集占据主流。为此，我们提出一个名为YoutubeFish-35的大规模数据集，共包含35段高帧率高清视频序列，覆盖多种选定场景，标注了超过15万个中心点。为开展基准测试，我们选取三种主流密集目标计数方法，在新收集的数据集上进行了细致评估。我们提出TransVidCount这一全新的强基线模型，它在统一框架内沿时域结合密度分支与回归分支，可有效解决难以辨识目标计数问题，在YoutubeFish-35数据集上取得了最优性能。
英文摘要：
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.
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