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Indexed by:期刊论文

Date of Publication:2017-03-01

Journal:IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY

Included Journals:SCIE、EI

Volume:27

Issue:3

Page Number:554-566

ISSN No.:1051-8215

Key Words:Laplacian matrix; low rank representation (LRR); product Grassmann manifold (PGM); subspace clustering

Abstract:In multicamera video surveillance, it is challenging to represent videos from different cameras properly and fuse them efficiently for specific applications such as human activity recognition and clustering. In this paper, a novel representation for multicamera video data, namely, the product Grassmann manifold (PGM), is proposed to model video sequences as points on the Grassmann manifold and integrate them as a whole in the product manifold form. In addition, with a new geometry metric on the product manifold, the conventional low rank representation (LRR) model is extended onto PGM and the new LRR model can be used for clustering nonlinear data, such as multicamera video data. To evaluate the proposed method, a number of clustering experiments are conducted on several multicamera video data sets of human activity, including the Dongzhimen Transport Hub Crowd action data set, the ACT 42 Human Action data set, and the SKIG action data set. The experiment results show that the proposed method outperforms many state-of-the-art clustering methods.