Leveraging human behavior models to predict paths in indoor environments

    Authors

    B. Tastan;G. Sukthankar

    Comments

    Authors: contact us about adding a copy of your work at STARS@ucf.edu

    Abbreviated Journal Title

    Pervasive Mob. Comput.

    Keywords

    Path prediction; Human tracking; Particle filters; OBSTACLE AVOIDANCE; TRACKING; Computer Science, Information Systems; Telecommunications

    Abstract

    One of the most powerful constraints governing many activity recognition problems is that imposed by the human actor. Iris well known that humans have a large set of physical and cognitive limitations that constrain their execution of various tasks. In this article, we show how prior knowledge of these perception and locomotion limitations can be exploited to enhance path prediction and tracking in indoor environments for pervasive computing applications. We demonstrate an approach for path prediction based on a model of visually guided steering that has been validated on human obstacle avoidance data. Our approach outperforms standard motion models in a particle filter tracker during occlusion periods of greater than one second and results in a significant reduction in SSD tracking error. (C) 2011 Elsevier B.V. All rights reserved.

    Journal Title

    Pervasive and Mobile Computing

    Volume

    7

    Issue/Number

    3

    Publication Date

    1-1-2011

    Document Type

    Article

    Language

    English

    First Page

    319

    Last Page

    330

    WOS Identifier

    WOS:000299669300004

    ISSN

    1574-1192

    Share

    COinS