Cascade method for image processing based people detection and counting

Zeyad Al-Zaydi, David Lorater Ndzi, David Adrian Sanders

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

Abstract

People detection is of great importance in video surveillance. Different approaches have been proposed to achieve accurate detection system. The main problem in people detection systems is that it must maintain a balance between the number of false detections and the number of missing people which limits the global detection results. In order to solve this problem and add robustness to detection, we propose a multiplexor and collector model composed of multiple independent detectors. This model is used to keep the true positive detections provided by a number of detectors and reduce the miss rate. In addition, a fusion model is proposed to check the robustness of the cascaded detection system. A pipeline techniques will also be used to avoid the increasing of detection time.
Original languageEnglish
Title of host publicationProceedings of 2016 International Conference on Image Processing, Production and Computer Science (ICIPCS'2016)
EditorsOsama Mohamed Mohamed Ahamed, Abhay Saxena
Place of PublicationLondon
PublisherUniversal Researchers in Civil and Architecture Engineering
Pages30-36
ISBN (Print)978-93-84422-71-4
DOIs
Publication statusPublished - 26 Mar 2016
EventInternational Conference on Image Processing, Production and Computer Science: ICIPCS-2016 - London, United Kingdom
Duration: 26 Mar 201627 Mar 2016

Conference

ConferenceInternational Conference on Image Processing, Production and Computer Science
Country/TerritoryUnited Kingdom
CityLondon
Period26/03/1627/03/16

Keywords

  • people detection
  • counting
  • surveillance systems
  • image processing
  • computer vision

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