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Abnormal trajectory detection for security infrastructure

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Abstract

In this work, an approach for the automatic analysis of people trajectories is presented, using a multi-camera and card reader system. Data is first extracted from surveillance cameras and card readers to create trajectories which are sequences of paths and activities. A distance model is proposed to compare sequences and calculate similarities. The popular unsupervised model One-Class Support Vector Machine (One-Class SVM) is used to train a detector. The proposed method classifies trajectories as normal or abnormal and can be used in two modes: off-line and real-time. Experiments are based on data simulation corresponding to an attack scenario proposed by a security expert. Results show that the proposed method successfully detects the abnormal sequences in the scenario with very low false alarm rate.
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Dates and versions

hal-02518529 , version 1 (25-03-2020)

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Van-Khoa Le, Pierre Beauseroy, Edith Grall-Maës. Abnormal trajectory detection for security infrastructure. the 2nd International Conference, Feb 2018, Tokyo, Japan. pp.1-5, ⟨10.1145/3193025.3193026⟩. ⟨hal-02518529⟩
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