Online learning neural tracker

S. Suresh, F. Brémond, M. Thonnat, H. J. Kim

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


Object tracking is a fundamental computer vision problem and is required for many high-level tasks such as activity recognition, behavior analysis and surveillance. The main challenge in the object tracking problem is the dynamic change in object/background appearance, illumination, shape and occlusion. We present an online learning neural tracker (OLNT) to differentiate the object from the background and also adapt to changes in object/background dynamics. For target modeling and object tracking, a neural algorithm based on risk sensitive loss function is proposed to handle issues related to sample imbalance and dynamics of object. Region-based features like region-based color moments for larger mobile objects and color/texture features at pixel level for smaller mobile objects are used to discriminate the object from background. The proposed neural classifier automatically determines the number of neurons required to estimate the posterior probability map. In the online learning neural classifier, only one neuron parameter is updated per tracker to reduce the computational burden during online adaptation. The tracked object is represented using an estimated posterior probability map. The posterior probability map is used to adapt the bounding box to handle the scale change and improper initialization.For illustrating the advantage of the proposed OLNT under rapid illumination variation, change in appearance, scale/size change, and occlusion, we present results from benchmark video sequences. Finally, we also present the comparison with well-known trackers in the literature and highlight the advantage of the proposed tracker.

Original languageEnglish
Pages (from-to)1681-1695
Number of pages15
Issue number10
Publication statusPublished - 2011 May

Bibliographical note

Funding Information:
The first author would like to thank Nanyang Technological University (NTU-SUG) Grant program, for their financial support to conduct this study. The authors would like to thank the reviewers for their comments and suggestions which has improved the quality of presentation.


  • Gaussian activation function
  • Neural classifier
  • Object tracking
  • Posterior probability map
  • Risk sensitive hinge loss function

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence


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