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Related Experiment Videos

Object Tracking Using Local Multiple Features and a Posterior Probability Measure.

Wenhua Guo1, Zuren Feng2, Xiaodong Ren3

  • 1Systems Engineering Institute, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, China. wh.guo@stu.xjtu.edu.cn.

Sensors (Basel, Switzerland)
|April 1, 2017
PubMed
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This study introduces a novel object tracking algorithm that combines a new local texture feature, simplified double center-symmetric local binary pattern (SDCS-LBP), with color information. The enhanced model improves tracking accuracy in challenging real-world scenarios with occlusions and illumination changes.

Area of Science:

  • Computer Vision
  • Machine Learning
  • Image Processing

Background:

  • Object tracking is difficult due to similar backgrounds, occlusions, and illumination changes.
  • Existing trackers struggle with complex real-world scenes.
  • Robust object representation and similarity measures are crucial for tracker performance.

Purpose of the Study:

  • To develop an improved object tracking algorithm addressing limitations of current methods.
  • To enhance object representation using a novel local texture feature.
  • To reduce matching errors using a posterior probability criterion.

Main Methods:

  • Introduced a new local texture feature: double center-symmetric local binary pattern (DCS-LBP).
  • Developed a simplified DCS-LBP (SDCS-LBP) for improved object texture modeling.
Keywords:
centroid iterationmultiple featuresobject trackingposterior probability measure

Related Experiment Videos

  • Combined SDCS-LBP and color for a multi-feature target model.
  • Utilized a posterior probability criterion and three update strategies for robust tracking.

    • Main Results:

    • The proposed SDCS-LBP feature demonstrates high discrimination and noise robustness.
    • The multi-feature model effectively describes object appearance.
    • The algorithm significantly improves tracking performance in challenging scenarios.
    • Experimental results show superiority over state-of-the-art methods.

    Conclusions:

    • The proposed centroid iteration algorithm with multi-features and posterior probability is effective for object tracking.
    • The SDCS-LBP feature offers a robust and discriminative representation for object tracking.
    • The method shows significant improvements in complex real-world scenarios.