Learning Behavioural Context |
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Authors: | Jian Li Shaogang Gong Tao Xiang |
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Affiliation: | 1.School of Electronic Engineering and Computer Science,Queen Mary University of London,London,UK |
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Abstract: | We propose a novel framework for automatic discovering and learning of behavioural context for video-based complex behaviour
recognition and anomaly detection. Our work differs from most previous efforts on learning visual context in that our model
learns multi-scale spatio-temporal rather than static context. Specifically three types of behavioural context are investigated:
behaviour spatial context, behaviour correlation context, and behaviour temporal context. To that end, the proposed framework consists of an activity-based semantic scene segmentation model for learning behaviour
spatial context, and a cascaded probabilistic topic model for learning both behaviour correlation context and behaviour temporal
context at multiple scales. These behaviour context models are deployed for recognising non-exaggerated multi-object interactive
and co-existence behaviours in public spaces. In particular, we develop a method for detecting subtle behavioural anomalies
against the learned context. The effectiveness of the proposed approach is validated by extensive experiments carried out
using data captured from complex and crowded outdoor scenes. |
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Keywords: | |
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