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Modelling Crowd Scenes for Event Detection

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Original languageEnglish
Title of host publicationPattern Recognition, 2006. ICPR 2006. 18th International Conference on
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages175-178
Number of pages4
ISBN (Print)0-7695-2521-0
DOIs
Publication statusPublished - 2006

Abstract

This work presents an automatic technique for detection of abnormal events in crowds. Crowd behaviour is difficult to predict and might not be easily semantically translated. Moreover it is difficulty to track individuals in the crowd using state of the art tracking algorithms. Therefore we characterise crowd behaviour by observing the crowd optical flow and use unsupervised feature extraction to encode normal crowd behaviour. The unsupervised feature extraction applies spectral clustering to find the optimal number of models to represent normal motion patterns. The motion models are HMMs to cope with the variable number of motion samples that might be present in each observation window. The results on simulated crowds demonstrate the effectiveness of the approach for detecting crowd emergency scenarios.

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