Robust Multiple Instance Learning Fast Compressive Tracking

Authors

  • Li Hong Wang Longqiao College of Lanzhou University of Finance and Economics, Lanzhou 730101
  • Rui Min Wu Longqiao College of Lanzhou University of Finance and Economics, Lanzhou 730101
  • Jin Lin Gao Longqiao College of Lanzhou University of Finance and Economics, Lanzhou 730101

DOI:

https://doi.org/10.26713/jims.v8i3.485

Keywords:

Object tracking, Fast compressive tracking, Multiple instance learning

Abstract

Fast compressive tracking algorithm performs more effective and robust than some other state-of-art tracking algorithm, it crop samples from the current frame, all these samples have the same weighted in learning procedure, in order to integrates the sample importance into the learning procedure, motived by the weighted multiple instance learning algorithm, we present a novel enhanced fast compressive tracking, which integrates the samples importance into learning procedure. Experimental results on various benchmark video sequences demonstrate the superior performance of our algorithm.

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Published

2016-12-01
CITATION

How to Cite

Wang, L. H., Wu, R. M., & Gao, J. L. (2016). Robust Multiple Instance Learning Fast Compressive Tracking. Journal of Informatics and Mathematical Sciences, 8(3), 201–208. https://doi.org/10.26713/jims.v8i3.485

Issue

Vol. 8 No. 3 (2016)

Section

Research Articles

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