Title

Convolutional Neural Network for Trajectory Prediction

Document Type

Conference Proceeding

Publication Date

9-8-2018

Publication Title

Computer Vision – ECCV 2018 Workshops

Publisher

European Conference on Computer Vision

Publisher Location

Munich, Germany

Volume

11131

First page number:

186

Last page number:

196

Abstract

Predicting trajectories of pedestrians is quintessential for autonomous robots which share the same environment with humans. In order to effectively and safely interact with humans, trajectory prediction needs to be both precise and computationally efficient. In this work, we propose a convolutional neural network (CNN) based human trajectory prediction approach. Unlike more recent LSTM-based moles which attend sequentially to each frame, our model supports increased parallelism and effective temporal representation. The proposed compact CNN model is faster than the current approaches yet still yields competitive results.

Keywords

Convolutional neural network; Trajectory prediction; Anticipating human behavior

Disciplines

Artificial Intelligence and Robotics | Computer Engineering

Language

English

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