KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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A novel visual tracking system with adaptive incremental extreme learning machine

  • Wang, Zhihui (National Key Laboratory, Hisense Company Limited) ;
  • Yoon, Sook (Department of Multimedia Engineering, Mokpo National University) ;
  • Park, Dong Sun (Division of Electronics Engineering, Chonbuk National University)
  • Received : 2016.03.07
  • Accepted : 2016.12.04
  • Published : 2017.01.31
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Abstract

This paper presents a novel discriminative visual tracking algorithm with an adaptive incremental extreme learning machine. The parameters for an adaptive incremental extreme learning machine are initialized at the first frame with a target that is manually assigned. At each frame, the training samples are collected and random Haar-like features are extracted. The proposed tracker updates the overall output weights for each frame, and the updated tracker is used to estimate the new location of the target in the next frame. The adaptive learning rate for the update of the overall output weights is estimated by using the confidence of the predicted target location at the current frame. Our experimental results indicate that the proposed tracker can manage various difficulties and can achieve better performance than other state-of-the-art trackers.

Keywords

Acknowledgement

Supported by : National Research Foundation of Korea(NRF)

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