The extended co-learning framework for robust object tracking

Chen Gong, Yang Liu, Tianyu Li, Jie Yang, Xiangjian He

Research output: Journal PublicationConference articlepeer-review

2 Citations (Scopus)


Recently, object tracking has been widely studied as a binary classification problem. Semi-supervised learning is particularly suitable for improving classification accuracy when large quantities of unlabeled samples are generated (just like tracking procedure). The purpose of this paper is to fulfill robust and stable tracking by using collaborative learning, which belongs to the scope of semi-supervised learning, among three classifiers. Different from [1], random fern classifier is incorporated to deal with 2bitBP feature newly added and certain constraints are specially implemented in our framework. Besides, the way for selecting positive samples is also altered by us in order to achieve more stable tracking. Algorithm proposed in this paper is validated by tracking pedestrian and cup under occlusion. Experiments and comparison show that our algorithm can avoid drifting problem to some degree and make tracking result more robust and adaptive.

Original languageEnglish
Article number6298430
Pages (from-to)398-403
Number of pages6
JournalProceedings - IEEE International Conference on Multimedia and Expo
Publication statusPublished - 2012
Externally publishedYes
Event2012 13th IEEE International Conference on Multimedia and Expo, ICME 2012 - Melbourne, VIC, Australia
Duration: 9 Jul 201213 Jul 2012


  • 2bitBP feature
  • collaborative learning
  • random fern classifier
  • semi-supervised learning
  • tracking

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications


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