Convolutional neural network with structural input for visual object tracking

Mustansar Fiaz, Arif Mahmood, Soon Ki Jung

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

7 Scopus citations

Abstract

Numerous deep learning approaches have been applied to visual object tracking owing to their capabilities to leverage huge training data for performance improvement. Most of these approaches have limitations with regard to learning target specific information rich features and therefore observe reduced accuracy in the presence of different challenges such as occlusion, scale variations, rotation and clutter. We proposed a deep neural network that takes input in the form of two stacked patches and regresses both the similarity and the dis-similarity scores in single evaluation. Image patches are concatenated depth-wise and fed to a six channel input of the network. The proposed network is generic and exploits the structural differences between the two input patches to obtain more accurate similarity and dissimilarity scores. Online learning is enforced via short-term and long-term updates to improve the tracking performance. Extensive experimental evaluations have been performed on OTB2015 and TempleColor128 benchmark datasets. Comparisons with state-of-the-art methods indicate that the proposed framework has achieved better tracking performance. The proposed tracking framework has obtained improved accuracy in different challenges including occlusion, background clutter, in-plane rotation and scale variations.

Original languageEnglish
Title of host publicationProceedings of the ACM Symposium on Applied Computing
PublisherAssociation for Computing Machinery
Pages1345-1352
Number of pages8
ISBN (Print)9781450359337
DOIs
StatePublished - 2019
Event34th Annual ACM Symposium on Applied Computing, SAC 2019 - Limassol, Cyprus
Duration: 8 Apr 201912 Apr 2019

Publication series

NameProceedings of the ACM Symposium on Applied Computing
VolumePart F147772

Conference

Conference34th Annual ACM Symposium on Applied Computing, SAC 2019
Country/TerritoryCyprus
CityLimassol
Period8/04/1912/04/19

Keywords

  • Convolutional neural network
  • Deep learning
  • Machine learning
  • Visual tracking

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