Fast Online Motion Segmentation through Multi-Temporal Interval Motion Analysis

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In this paper, we present a new algorithm for fast online motion segmentation with low time complexity. Feature points in each input frame of an image stream are represented as a spatial neighbor graph. Then, the affinities for each point pair on the graph, as edge weights, are computed through our effective motion analysis based on multi-temporal intervals. Finally, these points are optimally segmented by agglomerative hierarchical clustering combined with normalized modularity maximization. Through experiments on publicly available datasets, we show that the proposed method operates in real time with almost linear time complexity, producing segmentation results comparable with those of recent state-of-the-art methods.
Publisher
IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG
Issue Date
2015-02
Language
English
Article Type
Article
Citation

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, v.E98D, no.2, pp.479 - 484

ISSN
1745-1361
DOI
10.1587/transinf.2014EDL8123
URI
http://hdl.handle.net/10203/200070
Appears in Collection
EE-Journal Papers(저널논문)
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