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• W3003660333 abstract "We present a pedestrian tracking algorithm, DensePeds, that tracks individuals in highly dense crowds (>2 pedestrians per square meter). Our approach is designed for videos captured from front-facing or elevated cameras. We present a new motion model called Front-RVO (FRVO) for predicting pedestrian movements in dense situations using collision avoidance constraints and combine it with state-of-the-art Mask R-CNN to compute sparse feature vectors that reduce the loss of pedestrian tracks (false negatives). We evaluate DensePeds on the standard MOT benchmarks as well as a new dense crowd dataset. In practice, our approach is 4.5 × faster than prior tracking algorithms on the MOT benchmark and we are state-of-the-art in dense crowd videos by over 2.6% on the absolute scale on average." @default.
• W3003660333 created "2020-02-07" @default.
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• W3003660333 date "2019-11-01" @default.
• W3003660333 modified "2023-09-27" @default.
• W3003660333 title "DensePeds: Pedestrian Tracking in Dense Crowds Using Front-RVO and Sparse Features" @default.
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• W3003660333 doi "https://doi.org/10.1109/iros40897.2019.8968470" @default.
• W3003660333 hasPublicationYear "2019" @default.
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