Combination of Self-organization Map and Kernel Mutual Subspace method for video surveillance

Bailing Zhang, Junbum Park, Hanseok Ko

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper addresses the video surveillance issue of automatically identifying moving vehicles and people from continuous observation of image sequences. With a single far-field surveillance camera, moving objects are first segmented by simple background subtraction. To reduce the redundancy and select the representative prototypes from input video streams, the Self-organizing Feature Map (SOM) is applied for both training and testing sequences. The recognition scheme is designed based on the recently proposed Kernel Mutual Subspace (KMS) model. As an alternative to some probability-based models, KMS does not make assumptions about the data sampling processing and offers an efficient and robust classifier. Experiments demonstrated a highly accurate recognition result, showing the model's applicability in real-world surveillance system.

Original languageEnglish
Title of host publication2007 IEEE Conference on Advanced Video and Signal Based Surveillance, AVSS 2007 Proceedings
Pages123-128
Number of pages6
DOIs
Publication statusPublished - 2007 Dec 1
Event2007 IEEE Conference on Advanced Video and Signal Based Surveillance, AVSS 2007 - London, United Kingdom
Duration: 2007 Sep 52007 Sep 7

Other

Other2007 IEEE Conference on Advanced Video and Signal Based Surveillance, AVSS 2007
CountryUnited Kingdom
CityLondon
Period07/9/507/9/7

ASJC Scopus subject areas

  • Computer Science Applications
  • Signal Processing

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  • Cite this

    Zhang, B., Park, J., & Ko, H. (2007). Combination of Self-organization Map and Kernel Mutual Subspace method for video surveillance. In 2007 IEEE Conference on Advanced Video and Signal Based Surveillance, AVSS 2007 Proceedings (pp. 123-128). [4425297] https://doi.org/10.1109/AVSS.2007.4425297