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Unsupervised Analysis of Everyday Human Activities Using Suffix Trees

Traditional approaches for activity modeling assume prior knowledge about the structure of activities, based on which explicitly defined models are learned in a supervised manner. However, such activity structure is generally not completely known a priori. It is therefore imperative to find representations that facilitate learning of this structure with minimal supervision. Recent representational approaches to this end are limited by their computational complexity, and their ability to capture activity structure only up to some fixed temporal scale. In this work, we propose Suffix Trees as an activity representation to efficiently extract structure of activities by analyzing their constituent event-subsequences over multiple temporal scales. We prove how the feature-space induced by Suffix Trees is representationally superior to some of the previous approaches, and compare such approaches with Suffix Trees in terms of their discriminative power, noise sensitivity and unsupervised activity-class discovery. Moreover, exploiting certain properties of Suffix Trees, we present a novel perspective on anomalous subsequences of activities, and propose a linear-time algorithm for their automatic detection. We present comparative results using an extensive data-set of activities for cooking different recipes collected from multiple subjects in a household kitchen environment to demonstrate the competence of our framework.