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
Repository Citation
Nikhil, N.,
Morris, B. T.
(2018).
Convolutional Neural Network for Trajectory Prediction.
Computer Vision – ECCV 2018 Workshops, 11131
186-196.
Munich, Germany: European Conference on Computer Vision.
http://dx.doi.org/10.1007/978-3-030-11015-4_16