  Accurate prediction of future person location and movement trajectory from an
egocentric wearable camera can benefit a wide range of applications, such as
assisting visually impaired people in navigation, and the development of
mobility assistance for people with disability. In this work, a new egocentric
dataset was constructed using a wearable camera, with 8,250 short clips of a
targeted person either walking 1) toward, 2) away, or 3) across the camera
wearer in indoor environments, or 4) staying still in the scene, and 13,817
person bounding boxes were manually labelled. Apart from the bounding boxes,
the dataset also contains the estimated pose of the targeted person as well as
the IMU signal of the wearable camera at each time point. An LSTM-based
encoder-decoder framework was designed to predict the future location and
movement trajectory of the targeted person in this egocentric setting.
Extensive experiments have been conducted on the new dataset, and have shown
that the proposed method is able to reliably and better predict future person
location and trajectory in egocentric videos captured by the wearable camera
compared to three baselines.
