Egocentric Human Trajectory Forecasting with a Wearable Camera and Multi-Modal Fusion

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In this paper, we address the problem of forecasting the trajectory of an egocentric camera wearer (ego-person) in crowded spaces.

• The trajectory forecasting ability learned from the data of different camera wearers walking around in the real world can be transferred to assist visually impaired people in navigation, as well as to instill human navigation behaviours in mobile robots, enabling better human-robot interactions.
• To this end, a novel egocentric human trajectory forecasting dataset was constructed, containing real trajectories of people navigating in crowded spaces wearing a camera, as well as extracted rich contextual data.
• We extract and utilize three different modalities to forecast the trajectory of the camera wearer, i.e., his/her past trajectory, the past trajectories of nearby people, and the environment such as the scene semantics or the depth of the scene.