Detecting Individual Activities from Video in a Smart Home

Abstract

This paper addresses the detection of activities of individuals in a smart home environment. Our system is based on a robust video tracker that creates and tracks targets using a wide-angle camera. The system uses target position, size and orientation as input for interpretation. Interpretation produces activity labels such as “walking”, “standing”, “sitting”, “interacting with table”, or “sleeping” for each target. Bayesian Classifier and Support Vector Machines (SVMs) are compared for learning and recognizing previously defined individual activities. These methods are evaluated on recorded data sets. A novel Hybrid Classifier is then proposed. This classifier combines generative Bayesian methods and discriminative SVMs. Bayesian methods are used to detect previously unseen activities, while the SVMs are shown to provide high discriminative power for recognizing examples of learned activity classes. The evaluation results of the Hybrid classifier for the recorded data sets show that the combination of generative and discriminative classification methods outperforms the individual methods when identifying unseen activities.