Discriminative tracking via supervised tensor learning

Guoxia Xu, Sheheryar Khan, Hu Zhu*, Lixin Han, Kwok Po NG, Hong Yan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


Discriminative tracking algorithms have witnessed continued progress for distinguishing the target from background in unconstrained environments. The learning and detection task in existing visual tracking methods often convert a multidimensional data array into a vector-based observation. By altering the 2-D spatial structure of the image, transformation variants and global noises influence the discriminative ability of target representation, often result in degradation of performance. Different from vector representations, this paper presents a tensor-based large margin discriminative framework for visual tracking that utilizes the supervised tensor learning. In our method, an online structured support tensor classifier is designed which produces the multi-linear decision function, incorporating the nonlinearity of tensor-based feature over the target. In order to provide better spatial cues of target representation against noises and facilitate online tracking, we further introduce truncated tucker decomposition in structured multi-linear learning. The proposed algorithm poses an effective parameter tensor reconstruction in the classifier updating procedure and has a robust discriminative ability against several video background variants. Furthermore, a tensor block coordinate descent optimization is presented to achieve a closed form solution specific to the proposed truncated structured Tucker machine (TSTM). Experiment results on a recent comprehensive tracking benchmark demonstrate a promising performance of the proposed method subjectively and objectively compared with several state-of-the-art algorithms.

Original languageEnglish
Pages (from-to)33-47
Number of pages15
Publication statusPublished - 13 Nov 2018

Scopus Subject Areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

User-Defined Keywords

  • Tensor block coordinate descent
  • Tensor representation
  • Truncated structured tucker machine
  • Visual tracking


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