Fast Online Motion Segmentation through Multi-Temporal Interval Motion Analysis

Jungwon KANG  Myung Jin CHUNG  

IEICE TRANSACTIONS on Information and Systems   Vol.E98-D   No.2   pp.479-484
Publication Date: 2015/02/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2014EDL8123
Type of Manuscript: LETTER
Category: Image Recognition, Computer Vision
motion segmentation,  graph clustering,  motion preference set,  

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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.