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- W2211875450 abstract "Accurate real-time person detection is an important capability for many robot tasks, such as indoor navigation and human-robot interaction. In this paper, we introduce a depth-augmented, GPU-accelerated version of Deformable Parts Models (DPM) that uses a joint RGB+Depth feature descriptor to perform high-accuracy person detection at 5Hz while requiring less than 10 Watts on a single 2014 consumer-grade embedded chip. We provide a detailed description of the algorithm and evaluate its speed/accuracy trade-offs on an indoor person detection dataset collected from a mobile platform, showing that our RGBD approach outperforms accuracy of RGB-only DPM, depth-only DPM, and RGB HOG SVM classifier cascades. We furthermore demonstrate how reductions in model complexity and feature space dimensionality can increase speed without significantly sacrificing detector accuracy." @default.
- W2211875450 created "2016-06-24" @default.
- W2211875450 creator A5017982658 @default.
- W2211875450 date "2015-09-01" @default.
- W2211875450 modified "2023-09-25" @default.
- W2211875450 title "Depth-augmented Deformable Parts Models for RGBD person detection on embedded GPUs" @default.
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- W2211875450 doi "https://doi.org/10.1109/iros.2015.7354063" @default.
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