Designing activity detection systems that can be successfully deployed in
daily-living environments requires datasets that pose the challenges typical of
real-world scenarios. In this paper, we introduce a new untrimmed daily-living
dataset that features several real-world challenges: Toyota Smarthome Untrimmed
(TSU). TSU contains a wide variety of activities performed in a spontaneous
manner. The dataset contains dense annotations including elementary, composite
activities and activities involving interactions with objects. We provide an
analysis of the real-world challenges featured by our dataset, highlighting the
open issues for detection algorithms. We show that current state-of-the-art
methods fail to achieve satisfactory performance on the TSU dataset. Therefore,
we propose a new baseline method for activity detection to tackle the novel
challenges provided by our dataset. This method leverages one modality (i.e.
optic flow) to generate the attention weights to guide another modality (i.e
RGB) to better detect the activity boundaries. This is particularly beneficial
to detect activities characterized by high temporal variance. We show that the
method we propose outperforms state-of-the-art methods on TSU and on another
popular challenging dataset, Charades.

介绍了一种在日常生活环境中成功部署的活动检测系统，它使用了一个新的未修剪的日常生活数据集（TSU），包含了许多真实世界的挑战，如基本的、复合的活动以及涉及对象交互的活动，我们提供了对数据集的实际挑战分析，展示了当前最先进的方法无法满足该数据集的要求，并提出了一种新的基线方法，该方法利用一种模态（即光流）生成注意力权重，以指导另一种模态（即 RGB）更好地检测活动边界。