Visual tracking via Boolean map representations

Kaihua Zhang, Qingshan Liu, Jian Yang, Ming Hsuan Yang

Research output: Contribution to journalArticlepeer-review

32 Citations (Scopus)


In this paper, we present a simple yet effective Boolean map based representation that exploits connectivity cues for visual tracking. We describe a target object with histogram of oriented gradients and raw color features, of which each one is characterized by a set of Boolean maps generated by uniformly thresholding their values. The Boolean maps effectively encode multi-scale connectivity cues of the target with different granularities. The fine-grained Boolean maps capture spatially structural details that are effective for precise target localization while the coarse-grained ones encode global shape information that are robust to large target appearance variations. Finally, all the Boolean maps form together a robust representation that can be approximated by an explicit feature map of the intersection kernel, which is fed into a logistic regression classifier with online update, and the target location is estimated within a particle filter framework. The proposed representation scheme is computationally efficient and facilitates achieving favorable performance in terms of accuracy and robustness against the state-of-the-art tracking methods on the OTB50 and VOT2016 benchmark datasets.

Original languageEnglish
Pages (from-to)147-160
Number of pages14
JournalPattern Recognition
Publication statusPublished - 2018 Sept

Bibliographical note

Publisher Copyright:
© 2018 Elsevier Ltd

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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