Occlusion-aware pedestrian detection

Christos Apostolopoulos, Kamal Nasrollahi, M. Hsuan Yang, Mohammad N.S. Jahromi, Thomas B. Moeslund

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Failure in pedestrian detection systems can be extremely crucial, specifically in driverless driving. In this paper, failures in pedestrian detectors are refined by re-evaluating the results of state of the art pedestrian detection systems, via a fully convolutional neural network. The network is trained on a number of datasets which include a custom designed occluded pedestrian dataset to address the problem of occlusion. Results show that when applying the proposed network, detectors can not only maintain their state of the art performance, but they even decrease average false positives rate per image, especially in the case where pedestrians are occluded.

Original languageEnglish
Title of host publicationEleventh International Conference on Machine Vision, ICMV 2018
EditorsDmitry P. Nikolaev, Antanas Verikas, Petia Radeva, Jianhong Zhou
ISBN (Electronic)9781510627482
Publication statusPublished - 2019
Event11th International Conference on Machine Vision, ICMV 2018 - Munich, Germany
Duration: 2018 Nov 12018 Nov 3

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


Conference11th International Conference on Machine Vision, ICMV 2018

Bibliographical note

Publisher Copyright:
Copyright © 2019 SPIE.

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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