@inproceedings{8cc66fee0d5b49e0b986777194178f5e,
title = "Enhanced Gaze Following via Object Detection and Human Pose Estimation",
abstract = "The aim of gaze following is to estimate the gaze direction, which is useful for the understanding of human behaviour in various applications. However, it is still an open problem that has not been fully studied. In this paper, we present a novel framework for gaze following problem, where both the front/side face case and the back face case are taken into account. For the front/side face case, head pose estimation is applied to estimate the gaze, and then object detection is used to further refine the gaze direction by selecting the object that intersects with the gaze in a certain range. For the back face case, a deep neural network with the human pose information is proposed for gaze estimation. Experiments are carried out to demonstrate the superiority of the proposed method, as compared with the state-of-the-art method.",
keywords = "Deep neural network, Gaze following, Human pose estimation, Objection detection",
author = "Jian Guan and Liming Yin and Jianguo Sun and Shuhan Qi and Xuan Wang and Qing Liao",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 26th International Conference on MultiMedia Modeling, MMM 2020 ; Conference date: 05-01-2020 Through 08-01-2020",
year = "2020",
doi = "10.1007/978-3-030-37734-2\_41",
language = "英语",
isbn = "9783030377335",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "502--513",
editor = "Ro, \{Yong Man\} and Junmo Kim and Jung-Woo Choi and Wen-Huang Cheng and Wei-Ta Chu and Peng Cui and Min-Chun Hu and \{De Neve\}, Wesley",
booktitle = "MultiMedia Modeling - 26th International Conference, MMM 2020, Proceedings",
address = "德国",
}