Learning Trajectory Patterns by Clustering: Experimental Studies and Comparative Evaluation

Morris, Brendan; Trivedi, Mohan M.

doi:10.1109/CVPR.2009.5206559

Learning Trajectory Patterns by Clustering: Experimental Studies and Comparative Evaluation

Brendan Morris, Mohan M. Trivedi

CVPR 2009 pp. 312-319

doi:10.1109/CVPR.2009.5206559 /cvpr/2009/morris2009cvpr-learning/

Abstract

Recently a large amount of research has been devoted to automatic activity analysis. Typically, activities have been defined by their motion characteristics and represented by trajectories. These trajectories are collected and clustered to determine typical behaviors. This paper evaluates different similarity measures and clustering methodologies to catalog their strengths and weaknesses when utilized for the trajectory learning problem. The clustering performance is measured by evaluating the correct clustering rate on different datasets with varying characteristics.

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Cite

Text

Morris and Trivedi. "Learning Trajectory Patterns by Clustering: Experimental Studies and Comparative Evaluation." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2009. doi:10.1109/CVPR.2009.5206559

Markdown

[Morris and Trivedi. "Learning Trajectory Patterns by Clustering: Experimental Studies and Comparative Evaluation." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2009.](https://mlanthology.org/cvpr/2009/morris2009cvpr-learning/) doi:10.1109/CVPR.2009.5206559

BibTeX

@inproceedings{morris2009cvpr-learning,
  title     = {{Learning Trajectory Patterns by Clustering: Experimental Studies and Comparative Evaluation}},
  author    = {Morris, Brendan and Trivedi, Mohan M.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2009},
  pages     = {312-319},
  doi       = {10.1109/CVPR.2009.5206559},
  url       = {https://mlanthology.org/cvpr/2009/morris2009cvpr-learning/}
}