@inproceedings{f29367ba5870408e9d2fc6bd33a6da35,
title = "NS-SOF: A non-feature matching approach for sparse optical flow",
abstract = "This paper presents a novel approach to compute sparse optical flow field, which is different from the traditional feature matching methods such as SURF. The approach consists mainly of three novel parts. First, the improved PatchMatch that is tailored to sparse optical flow computation is used to generate NNF quickly. And the NNF is smoothed using a threshold filtering rather than global optimization for low complexity. Next, we use the superpixel method to segment the NNF and choose the representative optical flow for each segment using the statistical filtering. Finally, the outliers that are mistaken for inliers in the previous processing steps are removed using a global statistical filtering in the form of histograms. In the experiment, the approach is evaluated using real datasets provided by the KITTI benchmarks and compared with SURF and ORB. The result shows that our approach can generate more uniform sparse optical flow field with fewer outliers.",
keywords = "NNF, optical flow, sparse, statistical filtering, superpixel",
author = "Gang Hu and Yan Wang and Lei Guo",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2nd Asia-Pacific Conference on Intelligent Robot Systems, ACIRS 2017 ; Conference date: 16-06-2017 Through 18-06-2017",
year = "2017",
month = jul,
day = "19",
doi = "10.1109/ACIRS.2017.7986056",
language = "英语",
series = "2017 2nd Asia-Pacific Conference on Intelligent Robot Systems, ACIRS 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "13--18",
booktitle = "2017 2nd Asia-Pacific Conference on Intelligent Robot Systems, ACIRS 2017",
address = "美国",
}