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Object tracking with particles weighted by region proposal network

  • Qidan Zhu
  • Yanke Wang
  • Yunqian He
  • Xiao Hong
Article
  • 43 Downloads

Abstract

Most of existing particle filters suffer from computation of weights and almost all object detection networks have the risk of missing objects. Therefore, we propose a novel way to calculate the weight for each particle using the anchor scores output from region proposal network (RPN) in Faster RCNN. We first change the original anchor style in RPN slightly while training and then cast particles in feature space of VGG16 to do filtering both for center and scale. Without fully connected layers, it can lower the computational cost to a large extent and it can effectively maintain an accurate prediction of the posterior density using less than 30 particles. When increasing the number of particles, it is still capable to stay in a stable operating speed as there is no need to compute weights for particles a second time. Extensive experimental results on parts of OTB datasets and comparison with other methods demonstrate that the proposed tracker performs favorably both in location of object and the decision of scale.

Keywords

Object tracking Particle filter Region proposal network Anchor score 

Notes

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of Intelligent Control, College of AutomationHarbin Engineering UniversityHarbinChina

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