Wu, Hui-YinSantarra, TrevorLeece, MichaelVargas, RolandoJhala, ArnavChristie, Marc and Wu, Hui-Yin and Li, Tsai-Yen and Gandhi, Vineet2020-05-242020-05-242020978-3-03868-127-42411-9733https://doi.org/10.2312/wiced.20201131https://diglib.eg.org:443/handle/10.2312/wiced20201131Joint attention refers to the shared focal points of attention for occupants in a space. In this work, we introduce a computational definition of joint attention for the automated editing of meetings in multi-camera environments from the AMI corpus. Using extracted head pose and individual headset amplitude as features, we developed three editing methods: (1) a naive audio-based method that selects the camera using only the headset input, (2) a rule-based edit that selects cameras at a fixed pacing using pose data, and (3) an editing algorithm using LSTM (Long-short term memory) learned joint-attention from both pose and audio data, trained on expert edits. The methods are evaluated qualitatively against the human edit, and quantitatively in a user study with 22 participants. Results indicate that LSTM-trained joint attention produces edits that are comparable to the expert edit, offering a wider range of camera views than audio, while being more generalizable as compared to rule-based methods.smart conferencingautomated video editingjoint attentionLSTM10.2312/wiced.2020113137-37