Recognizing new activities with limited training data

Abstract

Activity recognition (AR) systems are typically built to recognize a predefined set of common activities. However, these systems need to be able to learn new activities to adapt to a user's needs. Learning new activities is especially challenging in practical scenarios when a user provides only a few annotations for training an AR model. In this work, we study the problem of recognizing new activities with a limited amount of labeled training data. Due to the shortage of labeled data, small variations of the new activity will not be detected resulting in a significant degradation of the system's recall. We propose the FE-AT (Feature-based and Attribute-based learning) approach, which leverages the relationship between existing and new activities to compensate for the shortage of the labeled data. We evaluate FE-AT on three public datasets and demonstrate that it outperforms traditional AR approaches in recognizing new activities, especially when only a few training instances are available.