Computer Science ›› 2020, Vol. 47 ›› Issue (12): 169-176.doi: 10.11896/jsjkx.191000021

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Robust Long-term Adaptive Object Tracking Based onMulti-correlation Filtering Strategy

TAN Jian-hao, YIN Wang, LIU Li-ming, WANG Yao-nan   

  1. College of Electrical and Information EngineeringHunan University Chansha 410082,China
    National Engineering Laboratory for Robot Visual Perception and Control Technology Changsha 410082,China
  • Received:2019-10-04 Revised:2020-03-07 Online:2020-12-15 Published:2020-12-17
  • About author:TAN Jian-hao ,born in 1962 Ph.D pro-fessor.His research interests include intelligent robot data mining pattern.recognitionsystem identification and image processing.
    YIN Wang ,born in 1995 master.His main research interests include machine vision robot Technology etc.

Abstract: The traditional correlation filtering methods have recently achieved excellent performance and shown great robustness to exhibiting motion blur and illumination changes.Howeverit is difficult to achieve tracking when the object has interference factors such as deformationcolor changeand heavy occlusion.It shows poor robustness when the object is lost and cannot be recovered to achieve long-term tracking.Thereforthis paper proposes a robust long-term object tracking algorithm.Firsta feature complementation strategy is proposedwhich linearly weights the feature responses of the directional gradient histogram and the global color histogramand learns a correlation filtering model that is robust to color changes and deformations to estimate the target displacement.Thenthe object features are taken to learn a discriminant correlation filter to maintain long-term memory of object appearance.We use the output responses of this model to determine if tracking failure occurs.We use the online SVM classifier to re-detect the lost objectand retrack the lost target which can effectively recover the tracking target from failure to achieve long-term tracking.In additionthis paper learns a correlation filter over a feature pyramid centered at the estimated object position for predicting scale changes and further enhance robustness and accuracy.Finallythis paper compares the proposed algorithm with the state-of-the-art performance tracking algorithms on the online object tracking benchmark.The result shows that the proposed algorithm performs great robustness and accuracy.

Key words: Color histogram, Correlation filter, Long-term object tracking, Scale adaptation, SVM re-detector

CLC Number: 

  • TP391.41
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