Convolutional Neural Network for Trajectory Prediction
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.
Cite
Text
Nikhil and Morris. "Convolutional Neural Network for Trajectory Prediction." European Conference on Computer Vision Workshops, 2018. doi:10.1007/978-3-030-11015-4_16Markdown
[Nikhil and Morris. "Convolutional Neural Network for Trajectory Prediction." European Conference on Computer Vision Workshops, 2018.](https://mlanthology.org/eccvw/2018/nikhil2018eccvw-convolutional/) doi:10.1007/978-3-030-11015-4_16BibTeX
@inproceedings{nikhil2018eccvw-convolutional,
title = {{Convolutional Neural Network for Trajectory Prediction}},
author = {Nikhil, Nishant and Morris, Brendan Tran},
booktitle = {European Conference on Computer Vision Workshops},
year = {2018},
pages = {186-196},
doi = {10.1007/978-3-030-11015-4_16},
url = {https://mlanthology.org/eccvw/2018/nikhil2018eccvw-convolutional/}
}