The task of action recognition in dark videos is useful in various scenarios, e.g., night surveillance and self-driving at night. Though progress has been made in the action recognition task for videos in normal illumination, few have studied action recognition in the dark. This is partly due to the lack of sufficient datasets for such a task. In this paper, we explored the task of action recognition in dark videos. We bridge the gap of the lack of data for this task by collecting a new dataset: the Action Recognition in the Dark (ARID) dataset. It consists of over 3,780 video clips with 11 action categories. To the best of our knowledge, it is the first dataset focused on human actions in dark videos. To gain further understandings of our ARID dataset, we analyze the ARID dataset in detail and exhibited its necessity over synthetic dark videos. Additionally, we benchmarked the performance of several current action recognition models on our dataset and explored potential methods for increasing their performances. Our results show that current action recognition models and frame enhancement methods may not be effective solutions for the task of action recognition in dark videos.
翻译:暗色视频中的行动识别任务在各种情景中都是有用的,例如夜间监控和夜间自驾等。虽然在正常光照中视频的行动识别任务方面取得了进展,但很少有人在黑暗中研究行动识别任务,这部分是由于缺少用于这一任务的足够数据集。在本文中,我们探讨了暗色视频中的行动识别任务。我们收集了一个新的数据集,即“暗色(ARID)中的行动识别数据集”,从而弥补了这项任务缺乏数据的差距。该数据集由3 780多个视频剪辑组成,共11个行动类别。据我们所知,这是第一个侧重于暗色视频中人类行动的数据集。为了进一步了解我们的“暗色(ARID)”数据集,我们详细分析“暗色(ARID)数据集,并展示其必要性。此外,我们将一些当前行动识别模型的性能作为基准,并探索提高它们性能的潜在方法。我们的成果显示,当前行动识别模型和框架增强方法可能不是暗色视频中行动识别任务的有效解决方案。