Home > Research > Publications & Outputs > Robust 3-D human detection in complex environme...

Links

Text available via DOI:

View graph of relations

Robust 3-D human detection in complex environments with a depth camera

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Published
  • L. Tian
  • M. Li
  • Y. Hao
  • Jun Liu
  • G. Zhang
  • Y.Q. Chen
Close
<mark>Journal publication date</mark>30/09/2018
<mark>Journal</mark>IEEE Transactions on Multimedia
Issue number9
Volume20
Number of pages13
Pages (from-to)2249-2261
Publication StatusPublished
Early online date7/02/18
<mark>Original language</mark>English

Abstract

Human detection has received great attention during the past few decades, which is yet still a challenging problem. In this paper, we focus on the problem of 3-D human detection, i.e., finding the human bodies and determining their 3-D coordinates in complex 3-D space using depth data only. Since the traditional sliding-window-based approaches for target localization are time-consuming and the recent deep-learning-based object detectors generate too many region proposals, we propose to utilize the candidate head-top locating stage to efficiently and quickly find the plausible head-top locations. In the second stage, we propose a Depth map, Multiorder depth template, and Height difference map representation encoding three channels of information for each candidate region to utilize the neural network pretrained on large-scale well-annotated datasets to classify the candidate regions. We evaluate our method on four publicly available challenging datasets. Extensive experimental results demonstrate that the proposed method is superior to the state-of-the-art methods while achieving real-time performance