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  • Research Article
  • Open Access

Incremental Support Vector Machine Framework for Visual Sensor Networks

EURASIP Journal on Advances in Signal Processing20062007:064270

  • Received: 4 January 2006
  • Accepted: 13 August 2006
  • Published:


Motivated by the emerging requirements of surveillance networks, we present in this paper an incremental multiclassification support vector machine (SVM) technique as a new framework for action classification based on real-time multivideo collected by homogeneous sites. The technique is based on an adaptation of least square SVM (LS-SVM) formulation but extends beyond the static image-based learning of current SVM methodologies. In applying the technique, an initial supervised offline learning phase is followed by a visual behavior data acquisition and an online learning phase during which the cluster head performs an ensemble of model aggregations based on the sensor nodes inputs. The cluster head then selectively switches on designated sensor nodes for future incremental learning. Combining sensor data offers an improvement over single camera sensing especially when the latter has an occluded view of the target object. The optimization involved alleviates the burdens of power consumption and communication bandwidth requirements. The resulting misclassification error rate, the iterative error reduction rate of the proposed incremental learning, and the decision fusion technique prove its validity when applied to visual sensor networks. Furthermore, the enabled online learning allows an adaptive domain knowledge insertion and offers the advantage of reducing both the model training time and the information storage requirements of the overall system which makes it even more attractive for distributed sensor networks communication.


  • Support Vector Machine
  • Sensor Node
  • Cluster Head
  • Incremental Learning
  • Error Reduction Rate

Authors’ Affiliations

IBM Systems and Technology Group, Department 7t Foundry, Essex Junction, VT 05452, USA
Department of Electrical and Computer Engineering, The University of Vermont, Burlington, VT 05405, USA


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© Mariette Awad et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.