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                            Library & Multimedia
 Online Human Interaction Detection and Recognition With Multiple Cameras
NCJ Number
              252346
Journal

*Transactions on Circuits and Systems for Video* Volume: 27 Issue: 3 Dated: 2017
Author(s)
          Saeid  Motiian;
                        Farzad  Siyahjani;
                        Ranya  Almohsen;
                        Gianfranco  Doretio
Date Published

2017
Length
              15 pages
Annotation
              This study addressed the problem of detecting and recognizing online the occurrence of human interactions recorded by a network of multiple cameras.
Abstract
              Interactions were represented by forming temporal trajectories, coupling together the body motion of each individual and her/his proximity relationships with others, and also sound whenever available. Such trajectories were modeled with kernel state-space (KSS) models. Their advantage is being suitable for the online interaction detection, recognition, and fusing information from multiple cameras, while enabling a fast implementation based on online recursive updates. In order to compare interaction trajectories in the space of KSS models, researchers designed so-called pairwise kernels with a special symmetry. For detection, researchers exploited the geometry of linear operators in Hilbert space, and extended to KSS models the concept of parity space, originally defined for linear models. For fusion, researchers combined KSS models with kernel construction and multiview learning techniques. Researchers evaluated the approach on four single-view publicly available data sets, and also introduced, and will make public, a new challenging human interactions data set collected using a network of three cameras. The results show that the approach has promise to become an effective building block for the analysis of real-time human behavior from multiple cameras. (Publisher abstract modified)
      
  
          Additional Details
Grant Number(s)
        2010-DD-BX-0161
                                2010-IJ-CX-K024


  
  
        
  Agencies
        NIJ-Sponsored


  
  
        
  Publication Type
        Research (Applied/Empirical)
                                Report (Study/Research)
                                Report (Grant Sponsored)
                                Program/Project Description


  
  
        
  Award
        Sensor, Surveillance, and Biometric Technologies Center of Excellence
    
  
  
Date Published: January 1, 2017
    
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      PatentsRelated Topics
        
                Camera technology
        Closed Circuit Television (CCTV)
        Computer aided operations
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        Criminology



  
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