Depth driven people counting using deep region proposal network

D. Song, Y. Qiao, A. Corbetta

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

9 Citations (Scopus)

Abstract

People counting is a crucial subject in video surveillance application. Factors such as severe occlusions, scene perspective distortions in real application scenario make this task challenging. In this paper, we carefully designed a deep detection framework based on depth information for people counting in crowded environments. Our system performs head detection on depth images collected by an overhead vertical Kinect sensor. To the best of our knowledge, this is the first attempt to use the deep convolutional neural networks on depth images for people counting. We explored the impact of the number and quality of RPN positive anchors on the performance of Faster R-CNN and proposed a solution. Our method is very simple but effective, not only showing promising results but also efficiency as it runs in real-time at a frame rate of about 110 frames per second on a GPU.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Information and Automation (ICIA), 18-20 July 2017, Macau, China
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages416-421
Number of pages6
ISBN (Electronic)978-1-5386-3154-6
ISBN (Print)978-1-5386-3155-3
DOIs
Publication statusPublished - 20 Oct 2017
Event2017 IEEE International Conference on Information and Automation, (ICIA 2017) - Macau, China
Duration: 18 Jul 201720 Jul 2017
http://2017.ieee-icia.org/

Conference

Conference2017 IEEE International Conference on Information and Automation, (ICIA 2017)
Abbreviated titleICIA 2017
CountryChina
CityMacau
Period18/07/1720/07/17
Internet address

Keywords

  • Convolutional Neural Network
  • Depth image
  • Head detection
  • People counting

Fingerprint Dive into the research topics of 'Depth driven people counting using deep region proposal network'. Together they form a unique fingerprint.

Cite this