Flipflop correlation tracking with Convolution Kernels Networks

Hui He, Bo Ma*, Luoyu Qin

*Corresponding author for this work

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

Abstract

Correlation filter-based tracking methods have accomplished competitive performance on accuracy and robustness, but there is still a huge potential in choosing suitable features. Recently, Convolutional Kernel Networks (CKN), which provide a fast and simple procedure to approximate kernel descriptors, have been proposed and achieved state-of-the-art performance in many vision tasks. In this paper, we present an adaptive tracker which integrates the kernel correlation filters with multiple effective CKN descriptors. By adopting a FlipFlop scheme, the weights of different features can be adjusted in the process of tracking to get better performance. Extensive experimental results on the OTB-2013 tracking benchmark show that our approach performs favorably against some representative state-of-the-art tracking algorithms.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1937-1941
Number of pages5
ISBN (Electronic)9781509041176
DOIs
Publication statusPublished - 16 Jun 2017
Event2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States
Duration: 5 Mar 20179 Mar 2017

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
Country/TerritoryUnited States
CityNew Orleans
Period5/03/179/03/17

Keywords

  • adaptive multiple features
  • convolutional kernel networks
  • correlation tracking

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