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- W2044339004 abstract "This paper proposes a very fast and robust multi-people tracking algorithm suitable for mobile platforms equipped with a RGB-D sensor. Our approach features a novel depth-based sub-clustering method explicitly designed for detecting people within groups or near the background and a three-term joint likelihood for limiting drifts and ID switches. Moreover, an online learned appearance classifier is proposed, that robustly specializes on a track while using the other detections as negative examples. Tests have been performed with data acquired from a mobile robot in indoor environments and on a publicly available dataset acquired with three RGB-D sensors and results have been evaluated with the CLEAR MOT metrics. Our method reaches near state of the art performance and very high frame rates in our distributed ROS-based CPU implementation." @default.
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- W2044339004 date "2012-10-01" @default.
- W2044339004 modified "2023-09-23" @default.
- W2044339004 title "Tracking people within groups with RGB-D data" @default.
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- W2044339004 doi "https://doi.org/10.1109/iros.2012.6385772" @default.
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