Robust visual tracking via convolutional networks without training

Kaihua Zhang, Qingshan Liu, Yi Wu, Ming Hsuan Yang

Research output: Contribution to journalArticlepeer-review

343 Citations (Scopus)


Deep networks have been successfully applied to visual tracking by learning a generic representation offline from numerous training images. However, the offline training is time-consuming and the learned generic representation may be less discriminative for tracking specific objects. In this paper, we present that, even without offline training with a large amount of auxiliary data, simple two-layer convolutional networks can be powerful enough to learn robust representations for visual tracking. In the first frame, we extract a set of normalized patches from the target region as fixed filters, which integrate a series of adaptive contextual filters surrounding the target to define a set of feature maps in the subsequent frames. These maps measure similarities between each filter and useful local intensity patterns across the target, thereby encoding its local structural information. Furthermore, all the maps together form a global representation, via which the inner geometric layout of the target is also preserved. A simple soft shrinkage method that suppresses noisy values below an adaptive threshold is employed to de-noise the global representation. Our convolutional networks have a lightweight structure and perform favorably against several state-of-the-art methods on the recent tracking benchmark data set with 50 challenging videos.

Original languageEnglish
Article number7410052
Pages (from-to)1779-1792
Number of pages14
JournalIEEE Transactions on Image Processing
Issue number4
Publication statusPublished - 2016 Apr

Bibliographical note

Publisher Copyright:
© 1992-2012 IEEE.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Graphics and Computer-Aided Design


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