This paper summarizes the TinyAction challenge which was organized in
ActivityNet workshop at CVPR 2021. This challenge focuses on recognizing
real-world low-resolution activities present in videos. Action recognition task
is currently focused around classifying the actions from high-quality videos
where the actors and the action is clearly visible. While various approaches
have been shown effective for recognition task in recent works, they often do
not deal with videos of lower resolution where the action is happening in a
tiny region. However, many real world security videos often have the actual
action captured in a small resolution, making action recognition in a tiny
region a challenging task. In this work, we propose a benchmark dataset,
TinyVIRAT-v2, which is comprised of naturally occuring low-resolution actions.
This is an extension of the TinyVIRAT dataset and consists of actions with
multiple labels. The videos are extracted from security videos which makes them
realistic and more challenging. We use current state-of-the-art action
recognition methods on the dataset as a benchmark, and propose the TinyAction
Challenge.

本论文总结了 TinyAction 挑战赛，该挑战赛是在 CVPR 2021 的 ActivityNet 研讨会上组织的，重点是识别视频中存在的真实低分辨率活动。我们提出了一个基准数据集 TinyVIRAT-v2，它由自然发生的低分辨率活动组成，利用数据集进行了当前最先进的动作识别方法。

TinyAction 挑战：在视频中识别真实世界低分辨率活动

TinyAction Challenge: Recognizing Real-world Low-resolution Activities  in Videos

The existing research in action recognition is mostly focused on high-quality
videos where the action is distinctly visible. In real-world surveillance
environments, the actions in videos are captured at a wide range of
resolutions. Most activities occur at a distance with a small resolution and
recognizing such activities is a challenging problem. In this work, we focus on
recognizing tiny actions in videos. We introduce a benchmark dataset,
TinyVIRAT, which contains natural low-resolution activities. The actions in
TinyVIRAT videos have multiple labels and they are extracted from surveillance
videos which makes them realistic and more challenging. We propose a novel
method for recognizing tiny actions in videos which utilizes a progressive
generative approach to improve the quality of low-resolution actions. The
proposed method also consists of a weakly trained attention mechanism which
helps in focusing on the activity regions in the video. We perform extensive
experiments to benchmark the proposed TinyVIRAT dataset and observe that the
proposed method significantly improves the action recognition performance over
baselines. We also evaluate the proposed approach on synthetically resized
action recognition datasets and achieve state-of-the-art results when compared
with existing methods. The dataset and code is publicly available at
this https URL

本研究介绍了一个新的数据集 TinyVIRAT，其中包含具有多个标签的低分辨率自然活动。为了识别这些 tiny actions in videos，我们提出了一种使用渐进生成方法来提高低分辨率动作质量的新方法。实验结果表明，在 TinyVIRAT 数据集上，我们的方法明显优于基线方法，并且在合成分辨率缩小的动作识别数据集上取得了 state-of-the-art 结果。