Looking deeper into Time for Activities of Daily Living Recognition

In this paper, we introduce a new approach for Activities of Daily Living (ADL) recognition. In order to discriminate between activities with similar appearance and motion, we focus on their temporal structure. Actions with subtle and similar motion are hard to disambiguate since long-range temporal information is hard to encode. So, we propose an end-to-end Temporal Model to incorporate long-range temporal information without losing subtle details. The temporal structure is represented globally by different temporal granularities and locally by temporal segments. We also propose a two-level pose driven attention mechanism to take into account the relative importance of the segments and granularities. We validate our approach on 2 public datasets: a 3D human activity dataset (NTU-RGB+D) and a human-object interaction dataset (Northwestern-UCLA Multiview Action 3D). Our Temporal Model can also be incorporated with any existing 3D CNN (including attention based) as a backbone which reveals its robustness.