This paper presents 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 also illustrate the 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: we are able to achieve more than 90% accuracy for two diverse types of datasets.