Multiview pedestrian localisation via a prime candidate chart based on occupancy likelihoods

Yuyao Yan, Ming Xu, Jeremy S. Smith

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

1 Citation (Scopus)


A sound way to localize occluded people is to project the foregrounds from multiple camera views to a reference view by homographies and find the foreground intersections. However, this may give rise to phantoms due to foreground intersections from different people. In this paper, each intersection region is warped back to the original camera view and is associated with a candidate box of the average size of pedestrians at that location. Then a joint occupancy likelihood is calculated for each intersection region. In the second step, essential candidate boxes are identified first, each of which covers at least a part of the foreground that is not covered by another candidate box. The non-essential candidate boxes are selected to cover the remaining foregrounds in the order of their joint occupancy likelihoods. Experiments on benchmark video datasets have demonstrated the good performance of our algorithm in comparison with other state-of-the-art methods.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781509021758
Publication statusPublished - 2017
Event24th IEEE International Conference on Image Processing, ICIP 2017 - Beijing, China
Duration: 17 Sept 201720 Sept 2017

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880


Conference24th IEEE International Conference on Image Processing, ICIP 2017


  • Image fusion
  • Image motion analysis
  • Object detection
  • Visual surveillance

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