DPDnet: A robust people detector using deep learning with an overhead depth camera

作者：

Highlights：

• Robust system to detect people only using depth information from a ToF camera.

• System outperforms state-of-the-art methods in different datasets without fine-tuning.

• Proposal runs in real time using conventional GPUs.

• Computational demands are independent of the number of people in the scene.

• Generated database is available to the research community.

摘要

•Robust system to detect people only using depth information from a ToF camera.•System outperforms state-of-the-art methods in different datasets without fine-tuning.•Proposal runs in real time using conventional GPUs.•Computational demands are independent of the number of people in the scene.•Generated database is available to the research community.

论文关键词：People detection,Depth camera information,Interest regions estimation,Overhead depth camera,Feature extraction

论文评审过程：Received 25 February 2019, Revised 28 October 2019, Accepted 27 December 2019, Available online 28 December 2019, Version of Record 11 January 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.113168