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.

翻译：设计能够在日常生活环境中成功部署的活动探测系统需要构成现实世界情景典型挑战的数据集。在本文中,我们引入了一个新的未剪辑的日常生活数据集,该数据集具有若干现实世界挑战的特点:丰田Smarthome Untracmed(TSU)。TSU包含以自发方式开展的多种多样的活动。该数据集包含密集的注释,包括基本的、复合的活动和与天体相互作用的活动。我们提供了对数据集所显示的现实世界挑战的分析,突出了探测算法的开放问题。我们显示,目前的最新方法无法在TSU数据集上取得令人满意的性能。因此,我们提出了新的活动探测基线方法,以应对我们数据集提供的新挑战。这种方法利用一种模式(即光学流)来引起人们的注意,以引导另一种模式(即RGB)更好地探测活动界限。这特别有利于探测具有高度时间差异的活动。我们提出了在TSU和另一种具有挑战性的数据设置上超越状态的方法。