@InProceedings{BrandãoWainGold:2006:SuHiPa,
               author = "Brand{\~a}o, Bruno Cedraz and Wainer, Jacques and Goldenstein, 
                         Siome Klein",
          affiliation = "UNICAMP",
                title = "Subspace Hierarchical Particle Filter",
            booktitle = "Proceedings...",
                 year = "2006",
               editor = "Oliveira Neto, Manuel Menezes de and Carceroni, Rodrigo Lima",
         organization = "Brazilian Symposium on Computer Graphics and Image Processing, 19. 
                         (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "tracking of objects, humans, articulated structures, particle 
                         filtering.",
             abstract = "Particle filtering has become a standard tool for non-parametric 
                         estimation in computer vision tracking applications. It is an 
                         instance of stochastic search. Each particle represents a possible 
                         state of the system. Higher concentration of particles at any 
                         given region of the search space implies higher probabilities. One 
                         of its major drawbacks is the exponential growth in the number of 
                         particles for increasing dimensions in the search space. We 
                         present a graph based filtering framework for hierarchical model 
                         tracking that is capable of substantially alleviate this issue. 
                         The method relies on dividing the search space in subspaces that 
                         can be estimated separately. Low correlated subspaces may be 
                         estimated with parallel, or serial, filters and have their 
                         probability distributions combined by a special aggregator filter. 
                         We describe a new algorithm to extract parameter groups, which 
                         define the subspaces, from the system model. We validate our 
                         method with different graph structures withing a simple hand 
                         tracking experiment with both synthetic and real data.",
  conference-location = "Manaus, AM, Brazil",
      conference-year = "8-11 Oct. 2006",
                  doi = "10.1109/SIBGRAPI.2006.42",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2006.42",
             language = "en",
                  ibi = "6qtX3pFwXQZG2LgkFdY/LNm7D",
                  url = "http://urlib.net/ibi/6qtX3pFwXQZG2LgkFdY/LNm7D",
           targetfile = "brandao-SHPF.pdf",
        urlaccessdate = "2024, May 03"
}