Pedestrian Detection via Mixture of CNN Experts and Thresholded Aggregated Channel Features

Ankit Verma, Ramya Hebbalaguppe, Lovekesh Vig, Swagat Kumar, Ehtesham Hassan

Research output: Chapter in Book/Report/Conference proceedingChapter

13 Citations (Scopus)

Abstract

In this paper, we propose a two stage pedestrian detector. The first stage involves a cascade of Aggregated Channel Features (ACF) to extract potential pedestrian windows from an image. We further introduce a thresholding technique on the ACF confidence scores that segregates candidate windows lying at the extremes of the ACF score distribution. The windows with ACF scores in between the upper and lower bounds are passed on to a Mixture of Expert (MoE) CNNs for more refined classification in the second stage. Results show that the designed detector yields better than state-of-the-art performance on the INRIA benchmark dataset and yields a miss rate of 10.35% at FPPI=0.1.
Original languageEnglish
Title of host publicationProceedings of the IEEE International Conference on Computer Vision
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages555-563
Number of pages9
ISBN (Print)9781467383905
DOIs
Publication statusPublished - 11 Feb 2015

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
Volume2015-February

Keywords

  • Convolutional codes
  • Deformable models
  • Detectors
  • Feature extraction
  • Neural networks
  • Support vector machines
  • Training

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  • Cite this

    Verma, A., Hebbalaguppe, R., Vig, L., Kumar, S., & Hassan, E. (2015). Pedestrian Detection via Mixture of CNN Experts and Thresholded Aggregated Channel Features. In Proceedings of the IEEE International Conference on Computer Vision (pp. 555-563). (Proceedings of the IEEE International Conference on Computer Vision; Vol. 2015-February). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCVW.2015.78