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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Ma, Y., & Li, M. (2007). Detection for abnormal event based on trajectory analysis and FSVM. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4681 LNCS, pp. 1112–1120). Springer Verlag. https://doi.org/10.1007/978-3-540-74171-8_113
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