@inproceedings{765712dfb4ac42d1ad8c545f949896fe,
title = "Dense optical flow estimation from RGB-D",
abstract = "Optical flow is a key problem in computer vision with tremendous potential applications in many fields, such as action recognition, autonomous navigation and manipulation. In this paper, we propose a dense optical flow estimation approach for objects of interest. In order to improve the accuracy of the optical flow estimation, the intensity and depth data from the RGB-D sensor are used for doing object segmentation. Afterwards, a homography based method assuming the surface to be planar is applied to obtain dense optical flow for each segment. Several experiments have been performed to evaluate the proposed method. The results demonstrate the validity of our approach.",
keywords = "Homography, Motion estimation, Optical flow, Segmentation",
author = "Jianxun Lv and Mohamed, \{Mahmoud A.\} and Haiwen Yuan",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 8th International Conference on Instrumentation and Measurement, Computer, Communication and Control, IMCCC 2018 ; Conference date: 19-07-2018 Through 21-07-2018",
year = "2018",
month = jul,
doi = "10.1109/IMCCC.2018.00076",
language = "英语",
series = "Proceedings - 8th International Conference on Instrumentation and Measurement, Computer, Communication and Control, IMCCC 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "328--333",
editor = "Jun-Bao Li",
booktitle = "Proceedings - 8th International Conference on Instrumentation and Measurement, Computer, Communication and Control, IMCCC 2018",
address = "美国",
}