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

^*Corresponding author for this work

Computer Science Research Group

Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences
Universiti Teknologi Nanyang
Chinese Academy of Sciences, Beijing

Research output: Contribution to journal › Letter, comment/opinion or interview › peer-review

80 Citations (Scopus)

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
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
Early online date	22 Jan 2010
DOIs	https://doi.org/10.1109/TSMCB.2009.2037923
Publication status	Published - 1 Feb 2011

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

biologically inspired
scene classification
video surveillance

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10.1109/TSMCB.2009.2037923

Cite this

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AU - Yuan, Yuan

AU - Li, Xuelong

AU - Tan, Tieniu

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Biologically inspired features for scene classification in video surveillance

Abstract

UN SDGs

Keywords

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