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Anti-social behavior detection in audio-visual surveillance systems
Kuklyte, Jogile; Kelly, Philip; Ó Conaire, Ciarán; O'Connor, Noel E.; Xu, Li-Qun
In this paper we propose a general purpose framework for detection of unusual events. The proposed system is based on the unsupervised method for unusual scene detection in web{cam images that was introduced in [1]. We extend their algorithm to accommodate data from different modalities and introduce the concept of time-space blocks. In addition, we evaluate early and late fusion techniques for our audio-visual data features. The experimental results on 192 hours of data show that data fusion of audio and video outperforms using a single modality.
Keyword(s): Machine learning; Signal processing; Algorithms; Image processing
Publication Date:
Type: Other
Peer-Reviewed: Unknown
Language(s): English
Institution: Dublin City University
Citation(s): Kuklyte, Jogile, Kelly, Philip, Ó Conaire, Ciarán, O'Connor, Noel E. ORCID: 0000-0002-4033-9135 <> and Xu, Li-Qun (2009) Anti-social behavior detection in audio-visual surveillance systems. In: PRAI*HBA - The Workshop on Pattern Recognition and Artificial Intelligence for Human Behaviour Analysis, 9-11 December 2009, Reggio Emilia, Italy.
File Format(s): application/pdf
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First Indexed: 2009-12-22 05:05:19 Last Updated: 2019-02-09 06:57:09