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Smart Environments

• Assisted Living

  In this project we develop a distributed vision-based smart home care system aimed for monitoring elderly persons and patients remotely. The cameras can continuously monitor the user or be triggered by a broadcast from a user badge when an accidental fall or other conditions are sensed by the badge. Tracking the approximate position of the user by the camera network allows for triggering cameras with the best views. Distributed scene analysis modules analyze the user’s posture and head location; these information are merged through a collaborative reasoning module, which makes a final decision about the type of the report that needs to be prepared. The developed prototype also allows for a voice channel to be established between the user badge and a call center over the phone line. By making use of different camera views, the number of false alarms is reduced, making the system more reliable and more efficient.

• Gesture Placement in Gaming and Virtual Reality

  Human pose estimation is a complex problem which can involve estimation of a large number of degrees of freedom for the model. In this project we explore a variety of multi-camera approaches based on feature and data fusion to develop a skeleton model replicating the pose and gestures of the user. The skeleton model contains the necessary information for placing the actions of the user in a virtual reality or gaming environment.
  
  
  
  
  
  
  
  
  
  
  
  

  • Smart Surface

    In this project we develop a multi-touch surface using multiple cameras. With an overhead camera and side-mounted cameras we can determine the x-y coordinates of the fingertips and detect touch events or measure hovering patterns, which are interpreted as commands for manipulating objects in computer presentations or applications.
    
    
    
    
    
    
    
    
    
    
    
    

    • Context-based Reasoning

      Until recently, computer vision has drawn heavily from signal theory. In general, classifiers have relied on local, low-level image statistics without considering surrounding objects. Natural images (i.e., those without content constraints) have a great deal of context which can be used to infer particular characteristics of objects in the scene. When multiple classifiers are run sequentially on the same image, they operate independently of one another. Intuitively however, we know that certain objects tend to be present together (e.g., spoons and forks, or tables and chairs), and are linked by well-defined spatial relationships.
      This project seeks to improve the performance of object classifiers by incorporating contextual information. For example, probability maps are used to guide classifiers to image regions likely to contain the object in question, based on the object's past positions and the positions of surrounding objects. Interactions between the user and the objects in the environment are used to establish behavior models, which can for example be used in assisted living applications to detect abnormal activity patterns.

      Publications

      Assisted Living

      • Distributed Vision-based Accident Management for Assisted Living

        H. Aghajan, J. Augusto, C. Wu, P. McCullagh, and J. Walkden, Int. Conf. on Smart homes and health Telematics (ICOST), June 2007.

      • Distributed Vision-Based Reasoning for Smart Home Care

        A. Keshavarz, A. Maleki-Tabar, and H. Aghajan, ACM SenSys Workshop on Distributed Smart Cameras (DSC), Oct. 2006.

      • Smart Home Care Network using Sensor Fusion and Distributed Vision-Based Reasoning

        A. Maleki-Tabar, A. Keshavarz, and H. Aghajan, ACM Multimedia Workshop On Video Surveillance and Sensor Networks (VSSN), Oct. 2006.

      Context-based Reasoning

      • Exploring the Relationship between Context and Pose: A Case Study

        I. Katz and H. Aghajan, Cognitive Systems and Interactive Sensors (COGIS), Nov. 2007.

      Virtual Reality, Gaming, and HCI

      • Toward Low Latency Gesture Control using Smart Camera Network (Link coming soon)

        Z. Zivkovic, V. Kliger, A. Danilin, B. Schueler, C. Chang, R. Kleihorst, and H. Aghajan, CVPR 2008 Workshop on Embedded Computer Vision (ECVW), June 2008.

      • Real-Time Human Posture Reconstruction in Wireless Smart Camera Networks (Link coming soon)

        C. Wu, R. Kleihorst, and H. Aghajan, Information Processing in Sensor Networks (IPSN-SPOTS), April 2008.

      • Pose and Gaze Estimation in Multi-Camera Networks for Non-Restrictive HCI

        C. Chang, C. Wu, and H. Aghajan, Int. Conf. on Computer Vision -- Workshop on HCI, Oct. 2007.

      • Linear Dynamic Data Fusion Techniques for Face Orientation Estimation in Smart Camera Networks

        C. Chang and H. Aghajan, 1st Int. Conf. on Distributed Smart Cameras (ICDSC), Sept. 2007.

      • Model-based Human Posture Estimation for Gesture Analysis in an Opportunistic Fusion Smart Camera Network

        C. Wu and H. Aghajan, Int. Conf. on Advanced Video and Signal based Surveillance (AVSS), Sept. 2007.

      • A Multi-Touch Surface Using Multiple Cameras

        I. Katz, K. Gabayan, and H. Aghajan, Advanced Concepts for Intelligent Vision Systems (ACIVS), August 2007.

      • Spatiotemporal Fusion Framework for Multi-Camera Face Orientation Analysis

        C. Chang and H. Aghajan, Advanced Concepts for Intelligent Vision Systems (ACIVS), August 2007.

      • Model-based Image Segmentation for Multi-View Human Gesture Analysis

        C. Wu and H. Aghajan, Advanced Concepts for Intelligent Vision Systems (ACIVS), August 2007.