The purpose of this paper is to address the problem of profiling human behaviour patterns captured in surveillance videos for the application of online normal behaviour recognition and anomaly detection.
A novel framework is developed for automatic behaviour profiling and online anomaly detection without any manual labeling of the training dataset.
Experimental results demonstrate the effectiveness and robustness of the authors' approach using noisy and sparse datasets collected from one real surveillance scenario.
To discover the topics, co‐clustering topic model not only captures the correlation between words, but also models the correlations between topics. The major difference between the conventional co‐clustering algorithms and the proposed CCMT is that CCMT shows a major improvement in terms of recall, i.e. interpretability.
Zhu, X. and Liu, Z. (2011), "Human behaviour profiling for anomaly detection", International Journal of Intelligent Computing and Cybernetics, Vol. 4 No. 3, pp. 366-379. https://doi.org/10.1108/17563781111160039Download as .RIS
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