@InProceedings{8081400,
  author = {T. Richmond and N. Lokare and E. Lobaton},
  booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
  title = {Robust trajectory-based density estimation for geometric structure recovery},
  year = {2017},
  pages = {1210-1204},
  abstract = {We propose a method to both quickly and robustly extract geometric information from trajectory data. While point density may be of interest in some applications, trajectories provide different guarantees about our data such as path densities as opposed to location densities provided by points. We aim to utilize the concise nature of quadtrees in two dimensions to reduce run time complexity of counting trajectories in a neighborhood. We compare the accuracy of our methodology to a common current practice for subsampling a structure. Our results show that the proposed method is able to capture the geometric structure. We find an improvement in performance over the current practice in that our method is able to extract only the salient data and ignore trajectory outliers.},
  keywords = {computational complexity;computational geometry;data mining;feature extraction;quadtrees;geometric structure recovery;trajectory data;point density;trajectory-based density estimation;geometric information extraction;quadtrees;run time complexity reduction;salient data extraction;Trajectory;Activity recognition;Three-dimensional displays;Europe;Signal processing;Estimation;Tools;Trajectory counting;density estimation;landmark selection;quadtree},
  doi = {10.23919/EUSIPCO.2017.8081400},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570347674.pdf},
}

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While point density may be of interest in some applications, trajectories provide different guarantees about our data such as path densities as opposed to location densities provided by points. We aim to utilize the concise nature of quadtrees in two dimensions to reduce run time complexity of counting trajectories in a neighborhood. We compare the accuracy of our methodology to a common current practice for subsampling a structure. Our results show that the proposed method is able to capture the geometric structure. We find an improvement in performance over the current practice in that our method is able to extract only the salient data and ignore trajectory outliers.","keywords":"computational complexity;computational geometry;data mining;feature extraction;quadtrees;geometric structure recovery;trajectory data;point density;trajectory-based density estimation;geometric information extraction;quadtrees;run time complexity reduction;salient data extraction;Trajectory;Activity recognition;Three-dimensional displays;Europe;Signal processing;Estimation;Tools;Trajectory counting;density estimation;landmark selection;quadtree","doi":"10.23919/EUSIPCO.2017.8081400","issn":"2076-1465","month":"Aug","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570347674.pdf","bibtex":"@InProceedings{8081400,\n author = {T. Richmond and N. Lokare and E. Lobaton},\n booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},\n title = {Robust trajectory-based density estimation for geometric structure recovery},\n year = {2017},\n pages = {1210-1204},\n abstract = {We propose a method to both quickly and robustly extract geometric information from trajectory data. While point density may be of interest in some applications, trajectories provide different guarantees about our data such as path densities as opposed to location densities provided by points. We aim to utilize the concise nature of quadtrees in two dimensions to reduce run time complexity of counting trajectories in a neighborhood. We compare the accuracy of our methodology to a common current practice for subsampling a structure. Our results show that the proposed method is able to capture the geometric structure. 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