Work

Using circular statistics for trajectory shape analysis

Year: 2008

Type: conference-paper

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.

Source: IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings

DOI: 10.1109/cvpr.2008.4587837

Authors: Andrea Prati, Simone Calderara, Rita Cucchiara

Institution: University of Modena and Reggio Emilia

Language: English

Topic: Data Management and Algorithms

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2160404135

FWCI: 5.256

Cites: 24

Cited by: 36

Related to: 10