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• W2019979159 abstract "Video cameras are widely used for monitoring public areas, such as train stations, airports and shopping centers. When crowds are dense, automatically tracking individuals becomes a challenging task. We propose a new tracker which employs a particle filter tracking framework, where the state transition model is estimated by an optical-flow algorithm. In this way, the state transition model directly uses the motion dynamics across the scene, which is better than the traditional way of a pre-defined dynamic model. Our result shows that the proposed tracker performs better on different tracking challenges compared with the state-of-the-art trackers, while also improving on the quality of the result." @default.
• W2019979159 created "2016-06-24" @default.
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• W2019979159 date "2012-05-01" @default.
• W2019979159 modified "2023-10-16" @default.
• W2019979159 title "Tracking individuals in surveillance video of a high-density crowd" @default.
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• W2019979159 doi "https://doi.org/10.1117/12.918604" @default.
• W2019979159 hasPublicationYear "2012" @default.
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