Biologically inspired features for scene classification in video surveillance

Kaiqi Huang; Dacheng Tao; Yuan Yuan; Xuelong Li; Tieniu Tan

doi:10.1109/TSMCB.2009.2037923

Biologically inspired features for scene classification in video surveillance

Kaiqi Huang
, Dacheng Tao
, Yuan Yuan
, Xuelong Li
, Tieniu Tan

CAS - Institute of Automation
Nanyang Technological University
Aston University
CAS - Xi'an Institute of Optics and Precision Mechanics

Research output: Contribution to journal › Article › peer-review

80 Scopus citations

Abstract

Inspired by human visual cognition mechanism, this paper first presents a scene classification method based on an improved standard model feature. Compared with state-of-the-art efforts in scene classification, the newly proposed method is more robust, more selective, and of lower complexity. These advantages are demonstrated by two sets of experiments on both our own database and standard public ones. Furthermore, occlusion and disorder problems in scene classification in video surveillance are also first studied in this paper.

Original language	English
Article number	5395619
Pages (from-to)	307-313
Number of pages	7
Journal	IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume	41
Issue number	1
DOIs	https://doi.org/10.1109/TSMCB.2009.2037923
State	Published - Feb 2011
Externally published	Yes

Keywords

Biologically inspired
scene classification
video surveillance

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10.1109/TSMCB.2009.2037923

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abstract = "Inspired by human visual cognition mechanism, this paper first presents a scene classification method based on an improved standard model feature. Compared with state-of-the-art efforts in scene classification, the newly proposed method is more robust, more selective, and of lower complexity. These advantages are demonstrated by two sets of experiments on both our own database and standard public ones. Furthermore, occlusion and disorder problems in scene classification in video surveillance are also first studied in this paper.",

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Biologically inspired features for scene classification in video surveillance

Abstract

Keywords

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