Using Circular Statistics for Trajectory Shape Analysis
Abstract
The analysis of patterns of movement is a crucial task for several surveillance applications, for instance to classify normal or abnormal people trajectories on the basis of their occurrence. This paper proposes to model the shape of a single trajectory as a sequence of angles described using a Mixture of Von Mises (MoVM) distribution. A complete EM (Expectation Maximization) algorithm is derived for MoVM parameters estimation and an on-line version proposed to meet real time requirement. Maximum-A-Posteriori is used to encode the trajectory as a sequence of symbols corresponding to the MoVM components. Iterative k-medoids clustering groups trajectories in a variable number of similarity classes. The similarity is computed aligning (with dynamic programming) two sequences and considering as symbol-to-symbol distance the Bhattacharyya distance between von Mises distributions. Extensive experiments have been performed on both synthetic and real data. ©2008 IEEE.
Cite
Text
Prati et al. "Using Circular Statistics for Trajectory Shape Analysis." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2008. doi:10.1109/CVPR.2008.4587837Markdown
[Prati et al. "Using Circular Statistics for Trajectory Shape Analysis." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2008.](https://mlanthology.org/cvpr/2008/prati2008cvpr-using/) doi:10.1109/CVPR.2008.4587837BibTeX
@inproceedings{prati2008cvpr-using,
title = {{Using Circular Statistics for Trajectory Shape Analysis}},
author = {Prati, Andrea and Calderara, Simone and Cucchiara, Rita},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2008},
doi = {10.1109/CVPR.2008.4587837},
url = {https://mlanthology.org/cvpr/2008/prati2008cvpr-using/}
}