Fall 2015 Teaching: Computer Vision and Computational Photography for Online MSCS.

August 15th, 2015 Irfan Essa Posted in Aaron Bobick, Computational Photography, Computer Vision No Comments »

In fall 2015 fall term, I am teaching two classes. Both for the Georgia Tech’s Online MSCS program.Cursor_and_CS6475___Computational_Photography___Georgia_Tech

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Paper in Artificial Intelligence (2009): “A novel sequence representation for unsupervised analysis of human activities”

September 20th, 2009 Irfan Essa Posted in Aaron Bobick, Activity Recognition, Charles Isbell, Papers, Raffay Hamid, Siddhartha Maddi No Comments »

A novel sequence representation for unsupervised analysis of human activities

  • R. Hamid, S. Maddi, A. Johnson, A. Bobick, I. Essa, and C. 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},
      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

Hamid et al AIJ Paper

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Thesis Raffay Hamid PhD (2008): “A Computational Framework For Unsupervised Analysis of Everyday Human Activities”

June 18th, 2008 Irfan Essa Posted in Aaron Bobick, Activity Recognition, Machine Learning, PhD, Raffay Hamid No Comments »

M. Raffay Hamid PhD (2008), “A Computational Framework For Unsupervised Analysis of Everyday Human Activities“, PhD Thesis, Georgia Institute of Techniology, College of Computing, Atlanta, GA. (Advisor: Aaron Bobick & Irfan Essa)

Abstract

In order to make computers proactive and assistive, we must enable them to perceive, learn, and predict what is happening in their surroundings. This presents us with the challenge of formalizing computational models of everyday human activities. For a majority of environments, the structure of the in situ activities is generally not known a priori. This thesis therefore investigates knowledge representations and manipulation techniques that can facilitate learning of such everyday human activities in a minimally supervised manner. 

A key step towards this end is finding appropriate representations for human activities. We posit that if we chose to describe activities as finite sequences of an appropriate set of events, then the global structure of these activities can be uniquely encoded using their local event sub-sequences. With this perspective at hand, we particularly investigate representations that characterize activities in terms of their fixed and variable length event subsequences. We comparatively analyze these representations in terms of their representational scope, feature cardinality and noise sensitivity.

Exploiting such representations, we propose a computational framework to 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 concise characterizations of these discovered activity-classes, both from a holistic as well as a by-parts perspective. Using such characterizations, we present an incremental method to classify

a new activity instance to one of the discovered activity-classes, and to automatically detect if it is anomalous with respect to the general characteristics of its membership class. Our results show the efficacy of our framework in a variety of everyday environments

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Paper: ICCV 2007, “Structure from Statistics – Unsupervised Activity Analysis using Suffix Trees”

October 15th, 2007 Irfan Essa Posted in Aaron Bobick, Activity Recognition, Aware Home, PAMI/ICCV/CVPR/ECCV, Papers, Raffay Hamid No Comments »

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.

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Paper: ACM IWVSSN (2006) “Unsupervised Analysis of Activity Sequences Using Event Motifs”

October 23rd, 2006 Irfan Essa Posted in AAAI/IJCAI/UAI, Aaron Bobick, Activity Recognition, Aware Home, Papers, Raffay Hamid, Siddhartha Maddi No Comments »

  • R. Hamid, S. Maddi, A. Bobick, I. Essa. “Unsupervised Analysis of Activity Sequences Using Event Motifs”, In proceedings of 4th ACM International Workshop on Video Surveillance and Sensor Networks (in conjunction with ACM Multimedia 2006).

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.

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