Authors:
Masashi Hatano
;
Ryo Hachiuma
and
Hideo Saito
Affiliation:
Graduate School of Science and Technology, Keio University, Yokohama, Japan
Keyword(s):
Trajectory Prediction, Egocentric Video.
Abstract:
In recent years, much attention has been paid to the prediction of pedestrian trajectories, as they are one of the key factors for a better society, such as automatic driving, a guide for blind people, and social robots interacting with humans. To tackle this task, many methods have been proposed but few are from the first-person perspective because of the lack of a publicly available dataset. Therefore, we propose a method that uses egocentric vision, which does not need to be trained with a first-person video dataset. We made it possible to utilize existing methods, which predict from a bird’s-eye view. In addition, we propose a novel way to consider semantic information without changing the shape of the input to apply to all existing bird’s-eye methods that use only past trajectories. Therefore, there is no need to create a new dataset from egocentric vision. The experimental results demonstrate that the proposed method makes it possible to predict from an egocentric view via exis
ting methods of bird’s-eye view. The proposed method qualitatively improves trajectory predictions without aggravating quantitative accracy, and the effectiveness of predicting the trajectories of multiple people simultaneously.
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