Detection for abnormal event based on trajectory analysis and FSVM

Authors:
Yongjun Ma;Mingqi Li
Affiliations:
College of Computer Science and Information Engineering, Tianjin University of Science and Technology, Tianjin, China;Editorial office of Journal of TUST, Tianjin, China
Venue:
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
Year:
2007

Citing 2
Cited 0

A fully automated content-based video search engine supporting spatiotemporal queries

IEEE Transactions on Circuits and Systems for Video Technology
Fuzzy support vector machines

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes an algorithm based on fuzzy support vector machine (FSVM), a new pattern analysis method, for detecting the abnormal trajectory patterns of moving objects from surveillance video. Firstly, feature points are extracted for presenting continuous trajectories. Then fuzzy memberships are introduced to measure contributions of the feature points of trajectory. Finally, the algorithm is applied to detect the abnormal patterns in 2D object trajectories. Experiments on trajectory data set show the validity of the algorithm.