Complex Video Scene Analysis Using Kernelized-Collaborative Behavior Pattern Learning Based on Hierarchical Representative Object Behaviors

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This paper considers an unsupervised learning algorithm that can automatically discover key behavior patterns to characterize a complex video scene. For behavior features (bFs) extracted at multiple spatial-temporal scales, an optimization problem is formulated to cluster bFs in their scales while establishing a collaborative nonlinear relationship in the form of a kernel regression function among clustered bFs across different spatial scales. The relationship allows features extracted in one scale to be considered as contextual information in the analysis of another scale. This optimization problem is solved using linear programming to reduce computational complexity. The proposed algorithm is evaluated on four crowded traffic scenes and two sports video data sets. Experimental results show that the proposed algorithm achieves a better performance compared with the current state-of-the-art algorithms in terms of video segmentation accuracy.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2017-06
Language
English
Article Type
Article
Keywords

EARTH-MOVERS-DISTANCE

Citation

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, v.27, no.6, pp.1275 - 1289

ISSN
1051-8215
DOI
10.1109/TCSVT.2016.2539540
URI
http://hdl.handle.net/10203/224543
Appears in Collection
EE-Journal Papers(저널논문)
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