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.
This research is sponsored by a grant of Tianjin Science&Technology Development Fundation of High School under contract 20061011.
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Ma, Y., Li, M. (2007). Detection for Abnormal Event Based on Trajectory Analysis and FSVM. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2007. Lecture Notes in Computer Science, vol 4681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74171-8_113
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DOI: https://doi.org/10.1007/978-3-540-74171-8_113
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74170-1
Online ISBN: 978-3-540-74171-8
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