Visual Tracking via Adaptive Tracker Selection with Multiple Features

  • Ju Hong Yoon
  • Du Yong Kim
  • Kuk-Jin Yoon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7575)


In this paper, a robust visual tracking method is proposed to track an object in dynamic conditions that include motion blur, illumination changes, pose variations, and occlusions. To cope with these challenges, multiple trackers with different feature descriptors are utilized, and each of which shows different level of robustness to certain changes in an object’s appearance. To fuse these independent trackers, we propose two configurations, tracker selection and interaction. The tracker interaction is achieved based on a transition probability matrix (TPM) in a probabilistic manner. The tracker selection extracts one tracking result from among multiple tracker outputs by choosing the tracker that has the highest tracker probability. According to various changes in an object’s appearance, the TPM and tracker probability are updated in a recursive Bayesian form by evaluating each tracker’s reliability, which is measured by a robust tracker likelihood function (TLF). When the tracking in each frame is completed, the estimated object’s state is obtained and fed into the reference update via the proposed learning strategy, which retains the robustness and adaptability of the TLF and multiple trackers. The experimental results demonstrate that our proposed method is robust in various benchmark scenarios.


Visual tracking multiple features transition probability matrix robust likelihood function tracker interaction appearance learning 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ju Hong Yoon
    • 1
  • Du Yong Kim
    • 2
  • Kuk-Jin Yoon
    • 1
  1. 1.Computer Vision Lab.Gwangju Institute of Science and TechnologyKorea
  2. 2.Applied Computing Lab.Gwangju Institute of Science and TechnologyKorea

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