An effective pipeline for pedestrian detection in mid-high density crowd

Yu Hao, Duke Gledhill, Ying Liu, Jiulun Fan, Zhijie Xu

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

Abstract

A novel framework is introduced to handle the pedestrian detection in mid-high crowd density. This framework exploits the samples obtained with the unsupervised approach, extracts the combined pattern of Histograms of Oriented Gradients (HOG) and Local Binary Pattern (LBP) for the training and detection. The motion information is utilized to adjust the confidence score of detect annotation, and the soft-NMS is used to handle the balance between the removing of redundant annotation and occlusion. The experiments indicate the proposed approach achieved a promising result compared to state-of-the-art trained with the benchmarking dataset.

Original languageEnglish
Title of host publicationICAC 2019 - 2019 25th IEEE International Conference on Automation and Computing
EditorsHui Yu
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781861376664
DOIs
Publication statusPublished - Sept 2019
Externally publishedYes
Event25th IEEE International Conference on Automation and Computing, ICAC 2019 - Lancaster, United Kingdom
Duration: 5 Sept 20197 Sept 2019

Publication series

NameICAC 2019 - 2019 25th IEEE International Conference on Automation and Computing

Conference

Conference25th IEEE International Conference on Automation and Computing, ICAC 2019
Country/TerritoryUnited Kingdom
CityLancaster
Period5/09/197/09/19

Keywords

  • HOG
  • LBP
  • Mid-high Crowd Density
  • Pedestrian Detection
  • Soft-NMS

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