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SportsGrounding.html
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<!DOCTYPE html>
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<title>SportsGrounding Dataset</title>
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<h3 class="banner-title">SportsGrounding Dataset</h3>
<h2 class="section-subtitle ">SportsGrounding: A Multi-Person Dataset for Spatio-temporal Video Grounding in Sports Videos</h2>
<p>✉<a href="">Runyu He</a>   ✉<a href="">Hanxiao Xie</a>   ✉<a href="http://wanglimin.github.io/">Limin Wang</a></p>
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<p><a href="http://mcg.nju.edu.cn/en/index.html">MCG Group @ Nanjing University</a></p>
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<img src="pics/sg1.png" alt="banner" class="banner-image">
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<!-- <small>Compared with three relation instances from VidVRD and AG datasets showed in the upper row, the bottom row shows interaction annotations in two sample keyframes of <i>SportsHHI</i>. The bounding boxes and interaction annotation of the same instance are displayed in the same color. <i>SportsHHI</i> provides complex multi-person scenes where various interactions between human pairs occur concurrently. It focuses on high-level interactions that require detailed spatio-temporal context reasoning.</small> -->
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<h3 class="section-title">Abstract</h3>
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<p>Given natural language queries and video clips, spatio-temporal video grounding task aims to output the most relevant segments and locate the corresponding regions in video clips. However, existing datasets have limited content regarding human actions and do not cover multi-person scenes. In this work, we propose a spatio-temporal video grounding dataset for sports videos, coined as <i>SportsGrounding</i>. We analyze the important components for constructing a realistic and challenging dataset for spatio-temporal video grounding by proposing two criteria: (1) grounding in multi-person scenes and motion-dependent contexts, and (2) well-defined boundaries. Based on these guidelines, we build the SportsGrounding v1.0 dataset by collecting 526 video clips of basketball category and annotating 4,479 instances with 113k bounding boxes. To benchmark this dataset, we adapt several baseline methods and provide an in-depth analysis of the results. We hope that our SportsGrounding will contribute to advancements in future methods in this field.</p>
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<p>Please choose "1080P" for better experience.</p>
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<h3 class="section-title">Data Construction</h3>
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<p>We provide 526 sports clips in the basketball category, utilizing video data from the MultiSports dataset, which are collected from the Olympic Games, NCAA Championship, and NBA on YouTube. We merge adjacent annotated instances of the same subject and provide corresponding natural language descriptions. These descriptions are unique throughout the video clip and clearly describe the visual attributes of the subjects (such as clothing, position, etc.), actions, and relationships, while also encouraging the interaction descriptions involving other individuals and relevant subjects on the court.</p>
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<h3 class="section-title">Dataset Statistics</h3>
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<h2 class="section-subtitle liner">Overall comparison of statistics between existing spatio-temporal video grounding datasets and our SportsGrounding v1.0</h2>
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<figure>
<img src="pics/sg2.png" alt="category" style="width: 65%; height: auto;">
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<p>The SportsGrounding dataset retains the uncut original long video data, with the number of complete videos associated with the annotated instances comparable to that of the HC-STVG dataset. Both the HC-STVG and SportsGrounding datasets have the longest average description lengths, with relatively rich content. In addition to appearance and actions, the text descriptions in the SportsGrounding dataset also include interaction information between the subjects and other players, which places higher demands on the model's scene modeling capabilities.</p>
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<h2 class="section-subtitle liner">Duration distribution of the SportsGrounding dataset</h2>
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<img src="pics/sg3.png" alt="category" style="width: 65%; height: auto;">
</figure>
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<p>The duration of instances in the SportsGrounding dataset ranges from approximately 0.2 seconds to over 16 seconds. Compared to the VidSTG and HC-STVG datasets, the duration differences between annotated instances in the SportsGrounding dataset are more pronounced, with generally shorter segment durations, which presents a greater challenge for spatio-temporal grounding.</p>
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<p>We employ the m_tIoU, m_sIoU and vIoU@R as evaluation criteria. The m_tIoU is the average temporal IoU between the selected clips and ground truth clips, the m_sloU is the average spatial IoU within the true start and end frames and voU@R is the proportion of samples which vloU > R.</p>
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<h3 class="section-title">Download</h3>
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<p>The SportsGrounding dataset will be open-sourced in the future.</p>
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