Electronic Science and Technology ›› 2020, Vol. 33 ›› Issue (3): 50-55.doi: 10.16180/j.cnki.issn1007-7820.2020.03.010

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Improved Kernelized Correlation Filter Tracking

ZENG Zhao,WU Wei,WANG Xin   

  1. School of Electronic Information,Hangzhou Dianzi University,Hangzhou 310018,China
  • Received:2019-02-14 Online:2020-03-15 Published:2020-03-25
  • Supported by:
    National Natural Science Foundation Fund:International (Regional) Cooperation and Exchange Funding Project(61411136003)


In order to solved the probelmof scale specificity and occlusion judgment failure of Kernel-correlation Filtering algorithm in target tracking, a position filter based on adaptive feature fusion was proposed to judge whether the target was occluded or not. When the Peak-to- Sidelobe Ratio anomaly was detected, the adaptive updating of the model was stopped and online re-detection was started, and the target size was determined by combining the scale filter in the scale pyramid, thus the accurate target location was obtained. The experiment evaluated the performance of the improved algorithm through 10 groups of motion video in complex background. Compared with the basic Kernel-correlation Filtering algorithm, the average center position error of the improved algorithm was reduced by 36.683 pixels; the average distance accuracy was increased by 44.632% when the threshold of the pixel was set to 20 pixels; and the overlap accuracy was increased by 46.453% when the boundary frame overlap threshold was set to 0.5.

Key words: target tracking, featurefusion, occlusiondiscrimination, modelupdate, scalefilter, translation filter

CLC Number: 

  • TP391.41