TR2004-030

Trajectory Distance Metric Using Hidden Markov Model Based Representation

    •  Porikli, F.M., "Trajectory Distance Metric Using Hidden Markov Model Based Representation", European Conference on Computer Vision (ECCV), May 2004.
      BibTeX TR2004-030 PDF
      • @inproceedings{Porikli2004may3,
      • author = {Porikli, F.M.},
      • title = {Trajectory Distance Metric Using Hidden Markov Model Based Representation},
      • booktitle = {European Conference on Computer Vision (ECCV)},
      • year = 2004,
      • month = may,
      • url = {https://www.merl.com/publications/TR2004-030}
      • }
  • Research Areas:

    Artificial Intelligence, Computer Vision, Machine Learning

Abstract:

In this paper, we introduce a set of novel distance metrics that use model based representations for trajectories. We determine the similarity of trajectories using the conformity of the corresponding HMM models. These metrics enable the comparison of tracjectories without any limitations of the conventional measures. They accurately identify the coordinate, orientation, and speed affinity. The proposed HMM based distance metrics can be used not only for ground truth comparisons but for clustering as well. Our experiments prove that they have superior discriminative properties.

 

  • Related News & Events

    •  NEWS    ECCV 2004: 3 publications by Matthew Brand
      Date: May 10, 2004
      Where: European Conference on Computer Vision (ECCV)
      MERL Contact: Matthew Brand
      Brief
      • The papers "Trajectory Pattern Detection by HMM Parameter Space features and Eigenvector Clustering" by Porikli, F.M., "Trajectory Distance Metric Using Hidden Markov Model Based Representation" by Porikli, F.M. and "Spectral Solution of Large-Scale Extrinsic Camera Calibration as a Graph Embedding Problem" by Brand, M., Antone, M. and Teller, S. were presented at the European Conference on Computer Vision (ECCV).
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