Human activity forecasting from videos in routine-based tasks is an open research problem that has numerous applications in robotics, visual monitoring and skill assessment. Currently, many challenges exist in activity forecasting because human actions are not fully observable from continuous recording. Additionally, a large number of human activities involve fine-grained articulated human motions that are hard to capture using frame-level representations. To overcome thesechallenges, we propose a method that forecasts human actions by learning the dynamics of local motion patterns extracted from dense trajectories using longshort-term memory (LSTM). The experiments on a pub-lic dataset validated the effectiveness of our proposed method in activity forecasting and demonstrate large improvements over the baseline two stream end-to-endmodel. We also learnt that human activity forecasting benefits from learning both the short-range motion pat-terns and long-term dependencies between actions.

Advancements in 360° cameras have increased their related livestreams. In the case of video conferencing, 360° cameras provide almost unrestricted visibility into a conference room for a remote viewer without the need for an articulating camera. However, local participants are left wondering if someone is connected and where remote participants might be looking. To address this, we fabricated a prototype device that shows the gaze and presence of remote 360° viewers using a ring of LEDs that match the remote viewports. We discuss the long term use of one of the prototypes in a lecture hall and present future directions for visualizing gaze presence in 360° video streams.