Context-aware Visual Analysis of Elderly Activity
We present a semi-supervised methodology for automatic recognition and classification of elderly activity
in a cluttered real home environment. The proposed mechanism recognizes elderly activities by using a semantic
model of the scene under visual surveillance. We use of trajectory data for unsupervised learning
of this scene context model. The model learning process does not involve any supervised feature selection and
does not require any prior knowledge about the scene. The learned model in turn de-fines the activity and
inactivity zones in the scene. An activity zone further contains block-level reference information, which is used to
generate features for semi-supervised classification using transductive support vector machines. We used very few
labeled examples for initial training. Knowledge of activity and inactivity zones improves the activity analysis
process in realistic scenarios significantly. Experiments on real-life videos have validated our approach.
Fall Detection Benchmark Sequences
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