Convolutional Neural Network for Trajectory Prediction

Autor:	Nikhil, Nishant, Morris, Brendan Tran
Rok vydání:	2018
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted at ECCV 2018 workshop - Anticipating Human Behavior
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1809.00696 Zobrazit plný text záznamu View this record from Arxiv