Path prediction is a fundamental task for estimating how pedestrians or vehicles are going to move in a scene. Because path prediction as a task of computer vision uses video as input, various information used for prediction, such as the environment surrounding the target and the internal state of the target, need to be estimated from the video in addition to predicting paths. Many prediction approaches that include understanding the environment and the internal state have been proposed. In this survey, we systematically summarize methods of path prediction that take video as input and and extract features from the video. Moreover, we introduce datasets used to evaluate path prediction methods quantitatively.
@article{arxiv.1811.00233,
title = {Survey on Vision-based Path Prediction},
author = {Tsubasa Hirakawa and Takayoshi Yamashita and Toru Tamaki and Hironobu Fujiyoshi},
journal= {arXiv preprint arXiv:1811.00233},
year = {2018}
}