Depth-Augmented Deformable Parts Models For Rgbd Person Detection On Embedded Gpus

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.