• Raffay Hamid, Siddhartha Maddi, Amos Johnson, Aaron Bobick, Irfan Essaand Charles Isbell (2009) “A novel sequence representation for unsupervised analysis of human activities” in Artificial Intelligence, Volume 173, Issue 14, September 2009, Pages 1221-1244. [PDF][DOI][Science Direct]

• Hamid, Maddi, Johnson, Bobick, Essa, and Isbell (2009), “A Novel Sequence Representation for Unsupervised Analysis of Human Activities,” Artificial Intelligence Journal, 2009. [BIBTEX]

  @article{2009-Hamid-NSRUAHA,
    Author = {R. Hamid and S. Maddi and A. Johnson and A. Bobick and I. Essa and C. Isbell},
    Date-Modified = {2011-12-08 21:27:48 +0000},
    Journal = {Artificial Intelligence Journal},
    Month = {May},
    Title = {A Novel Sequence Representation for Unsupervised Analysis of Human Activities},
    Year = {2009}}

Abstract

Formalizing computational models for everyday human activities remains an open challenge. Many previous approaches towards this end assume prior knowledge about the structure of activities, using which explicitly defined models are learned in a completely supervised manner. For a majority of everyday environments however, the structure of the in situ activities is generally not known a priori. In this paper we investigate knowledge representations and manipulation techniques that facilitate learning of human activities in a minimally supervised manner. The key contribution of this work is the idea that global structural information of human activities can be encoded using a subset of their local event subsequences, and that this encoding is sufficient for activity-class discovery and classification.

In particular, we investigate modeling activity sequences in terms of their constituent subsequences that we call event n-grams. Exploiting this representation, we propose a computational framework to automatically discover the various activity-classes taking place in an environment. We model these activity-classes as maximally similar activity-cliques in a completely connected graph of activities, and describe how to discover them efficiently. Moreover, we propose methods for finding characterizations of these discovered classes from a holistic as well as a by-parts perspective. Using such characterizations, we present a method to classify a new activity to one of the discovered activity-classes, and to automatically detect whether it is anomalous with respect to the general characteristics of its membership class. Our results show the efficacy of our approach in a variety of everyday environments.

Keywords: Temporal reasoning; Scene analysis; Computer vision

FEATURE AT A GLANCE: Technology in the home environment has the potential to support older adults in a variety of ways. We took an interdisciplinary approach (human factors/ergonomics and computer science) to develop a technology “coach” that could support older adults in learning to use a medical device. Our system provided a computer vision system to track the use of a blood glucose meter and provide users with feedback if they made an error. This research could support the development of an in-home personal assistant to coach individuals in a variety of tasks necessary for independent living.

KEYWORDS: home technology, medical devices, support for learning

Abstract

Models of activity structure for unconstrained environments are generally not available a priori. Recent representational approaches to this end are limited by their computational complexity, and 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 empirically compare Suffix Trees with some of the previous approaches in terms of feature cardinality, discriminative prowess, noise sensitivity and activity-class discovery. Finally, exploiting properties of Suffix Trees, we present a novel perspective on anomalous subsequences of activities, and propose an algorithm to detect them in linear-time. We present comparative results over experimental data, collected from a kitchen environment to demonstrate the competence of our proposed framework.

Abstract

We present an unsupervised framework to discover characterizations of everyday human activities, and demonstrate how such representations can be used to extract points of interest in event-streams. We begin with the usage of Suffix Trees as an efficient activity-representation to analyze the global structural information of activities, using their local event statistics over the entire continuum of their temporal resolution. Exploiting this representation, we discover characterizing event-subsequences and present their usage in an ensemble-based framework for activity classification. Finally, we propose a method to automatically detect subsequences of events that are locally atypical in a structural sense. Results over extensive data-sets, collected from multiple sensor-rich environments are presented, to show the competence and scalability of the proposed framework.

Abstract

Graphical models are often used to represent and recognize activities. Purely unsupervised methods (such as HMMs) can be trained automatically but yield models whose internal structure – the nodes – are difficult to interpret semantically. Manually constructed networks typically have nodes corresponding to sub-events, but the programming and training of these networks is tedious and requires extensive domain expertise. In this paper, we propose a semi-supervised approach in which a manually structured, Propagation Network (a form of a DBN) is initialized from a small amount of fully annotated data, and then refined by an EM-based learning method in an unsupervised fashion. During node refinement (the M step) a boosting-based algorithm is employed to train the evidence detectors of individual nodes. Experiments on a variety of data types – vision and inertial measurements – in several tasks demonstrate the ability to learn from as little as one fully annotated example accompanied by a small number of positive but non-annotated training examples. The system is applied to both recognition and anomaly detection tasks.

ABSTRACT

We present an approach for tracking varying number of objects through both temporally and spatially significant occlusions. Our method builds on the idea of object permanence to reason about occlusions. To this end, tracking is performed at both the region level and the object level. At the region level, a customized genetic algorithm is used to search for optimal region tracks. This limits the scope of object trajectories. At the object level, each object is located based on adaptive appearance models, spatial distributions and inter-occlusion relationships. The proposed architecture is capable of tracking objects even in the presence of long periods of full occlusions. We demonstrate the viability of this approach by experimenting on several videos of a user interacting with a variety of objects on a desktop.

Temporal Reasoning from Video to Temporal Synthesis of Video

Abstract

In this talk, I will present some ongoing work on extracting spatio-temporal cues from video for both synthesis of novel video sequences, and recognition of complex activities. I will start off with some of our earlier work on Video Textures, where repeating information is extracted to generate extended sequences of videos. I will then describe some of our extensions to this approach that allow for controlled generation of animations of video sprites. We have developed various learning and optimization techniques that allow for video-based animations of photo-realistic characters. Then I will describe our new approach for image and video synthesis that builds on optimal patch-based copying of samples. I will show how our method allows for iterative refinement and extends to synthesis of both images and video from very limited samples. In the next part of my talk, I will describe how a similar analysis of video can be used to recognize what a person is doing in a scene. Such an analysis of video, aimed at recognition, requires more contextual information about the environment. I will show how we leverage contextual information shared between actions and objects to recognize what is happening in complex environments. I will also show that by adding some form of grammar (we use Stochastic Context Free Grammar) we can recognize very complex, multi-tasked activities.

If time permits, I will describe (very briefly) the Aware Home project at Georgia Tech, which is one primary area of ongoing and future research for me and my group. Further information on my work with videos is available from my webpage at http://www.cc.gatech.edu/~irfan

Abstract

We present propagation networks (P-nets), a novel approach for representing and recognizing sequential activities that include parallel streams of action. We represent each activity using partially ordered intervals. Each interval is restricted by both temporal and logical constraints, including information about its duration and its temporal relationship with other intervals. P-nets associate one node with each temporal interval. Each node is triggered according to a probability density function that depends on the state of its parent nodes. Each node also has an associated observation function that characterizes supporting perceptual evidence. To facilitate real-time analysis, we introduce a particle filter framework to explore the conditional state space. We modify the original condensation algorithm to more efficiently sample a discrete state space (D-condensation). Experiments in the domain of blood glucose monitor calibration demonstrate both the representational power of P-nets and the effectiveness of the D-condensation algorithm.

ABSTRACT

The focus of this project is on development of a domestic environment that is cognizant of the whereabouts and activities of its occupants and can support them in their everyday life. While the technology is applicable to a range of domestic situations, the emphasis in this work will be on support for aging in place; through collaboration with experts in assistive care and cognitive aging, the PI and his team will design, demonstrate, and evaluate a series of domestic services that aim to maintain the quality of life for an aging population, with the goal of increasing the likelihood of a “stay at home” alternative to assisted living that satisfies the needs of an aging individual and his/her distributed family. In particular, the PI will explore two areas that are key to sustaining quality of life for an independent senior adult: maintaining familial vigilance, and supporting daily routines. The intention is to serve as an active partner, aiding the senior occupant without taking control. This research will lead to advances in three research areas: human-computer interaction; computational perception; and software engineering. To achieve the desired goals, the PI will conduct the research and experimentation in an authentic domestic setting, a novel research facility called the Residential Laboratory recently completed next to the Georgia Tech campus. Together with experts in theoretical and practical aspects of aging, the PI will establish a pattern of research in which informed design of ubiquitous computing technology can be rapidly deployed, evaluated and evolved in an authentic setting. Special attention will be paid throughout to issues relating to privacy and trust implications. The PI will transition the products of this project to researchers and practitioners interested in performing more large-scale observations of the social and economic impact of Aware Home technologies.

April 5, 2001 Issue

Quote from the Article: “Cameras are going to rule one day at the Georgia Tech house, though, staff members there say. Dr. Irfan A. Essa, a computer science professor at Georgia Tech, is one of the people building a tracking system, based on video cameras, that will one day replace radio frequency tags. ”We can locate where the person is,” Dr. Essa said, ”and make a first-level guess at where this person is heading using the optical sensors.””

Essa, I.A (2000), “Ubiquitous sensing for smart and aware environments” In Personal Communications, IEEE [see also IEEE Wireless Communications] Publication Date: Oct. 2000, Volume: 7 , Issue: 5
On page(s): 47 – 49, ISSN: 1070-9916, CODEN: IPCME7, INSPEC Accession Number:6756447, Digital Object Identifier: 10.1109/98.878538

Abstract

As computing technology continues to become increasingly pervasive and ubiquitous, we envision the development of environments that can sense what we are doing and support our daily activities. In this article, we outline our efforts toward building such environments and discuss the importance of a sensing and signal-understanding infrastructure that leads to awareness of what is happening in an environment and how it can best be supported. Such an infrastructure supports both high- and low-end data transmission and processing, while allowing for detailed interpretation, modeling and recognition from sensed information. We are currently prototyping several aware environments to aid in the development and study of such sensing and computation in real-world settings

Abstract

We are building a home, called the Aware Home, to create a living laboratory for research in ubiquitous computing for everyday activities. This paper introduces the Aware Home project and outlines some of our technology-and human-centered research objectives in creating the Aware Home.

Is it possible to create a home environment that is aware of its occupants whereabouts and activities?

If we build such a home, how can it provide services to its residents that enhance their quality of life or help them to maintain independence as they age? The Aware Home Research Initiative (AHRI) is an interdisciplinary research endeavor at Georgia Tech aimed at addressing the fundamental technical, design, and social challenges presented by such questions.

The Aware Home Research Initiative at Georgia Institute of Technology is devoted to the multidisciplinary exploration of emerging technologies adn services based in the home. Starting in 1988, our collection of faculty and students has created a unique research facility that allows us to simulate and evaluate user experiences with off-the-shelf and state-of-the-art technologies. With speciifc expertise in health, education, entertainment and usable security, we are able to apply our research to problems of significant social and economic impact.

New technologies show great promise when applied to the home domain. The opportunities are vast, ranging from new modes of entertainment, services to simplify the management of the home and its myriad activities, and much-needed assistance for individuals at risk and the busy family members who care for them.

Home entertainment is important to help us enjoy our leisure time. We are interested in developingg new ways to simplify the control of a complex array of digital entertainment items and to creat new ways to capture the meaning ful moments of everyday life and share them with others now and well into the future. As we introduce more technologies into the home, we do not want to change the important characteristic of home life; to relax and enjoy family events. Currently, the influx of technology into the home has produced an increased burden to manage that infrastructure and guard against new security threats. by considering the importance of the human experience in managing technology and maintaining control and privacy, we are showing how a state-of-the-art experience can also be an enjoyable one.

Many otherwise busy adults are sandwiched between generations of older and younger relations that rely on them for care. Many baby boomers take responsibility to help an aging parent retain an independent life in his or her own home, rather than moving to an institutional facility. Others are assisting a developmentally delayed child or grandchild grow into an independent life in his or her own hoem, rather than moving to an institutional facility. Others are assisting a developmentally delayed child or grandchild grow into an independent and functional lifestyle. Still others may help a sibling cope with a chronic health condition. Whatever the situation, there are many opportunities for home technologies to support the important communication and coordination tasks a network of formal and informal caregivers. the same technologies that revolutionized and “flattened” the workplace can now make life easier in the home.