Robust object tracking by interleaving variable rate color particle filtering and deep learning

B. Akok, F. Gurkan, O. Kaplan, B. Gunsel

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

7 Citations (Scopus)

Abstract

We propose an effective combination of discriminative and generative tracking approaches in order to take the benefits from both. Our algorithm exploits the discriminative properties of Faster R-CNN which helps to generate target specific region proposals. A new proposal distribution is formulated to incorporate information from the dynamic model of moving objects and the detection hypotheses generated by deep learning. We construct the generative appearance model from the region proposals and perform tracking through sequential Bayesian filtering by variable rate color particle filtering (VRCPF). Test results reported on CVPR2013 benchmarking data set demonstrate that the interleaving of tracker and detector enables us to effectively update the target distribution that significantly improves robustness to illumination changes, scale changes, high motion and occlusion.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
PublisherIEEE Computer Society
Pages3665-3669
Number of pages5
ISBN (Electronic)9781509021758
DOIs
Publication statusPublished - 2 Jul 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
Volume2017-September
ISSN (Print)1522-4880

Conference

Conference24th IEEE International Conference on Image Processing, ICIP 2017
Country/TerritoryChina
CityBeijing
Period17/09/1720/09/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

  • Deep learning
  • Object tracking
  • Particle filtering

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