Biologically Inspired Features for Scene Classification in Video Surveillance

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...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2011-02, Vol.41 (1), p.307-313
Main Authors: Huang, Kaiqi, Tao, Dacheng, Yuan, Yuan, Li, Xuelong, Tan, Tieniu
Format: Article
Language:English
Subjects:
Artificial Intelligence
Bayes Theorem
Biologically inspired
Classification
Cognition
Cybernetics
Databases, Factual
Disorders
Feature extraction
Human
Humans
Image Processing, Computer-Assisted - methods
Image recognition
Image segmentation
Laboratories
Layout
Models, Biological
Pattern recognition
Pattern Recognition, Automated - methods
Population Surveillance - methods
Robustness
scene classification
Surveillance
Video Recording - methods
Video surveillance
Visual
Visual Perception
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Summary: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.
ISSN:1083-4419
2168-2267
1941-0492
2168-2275
DOI:10.1109/TSMCB.2009.2037923