A Method of Sample Updating and Target Repositioning Based on KCF

doi:10.3969/j.issn.1006-2475.2020.01.021

Abstract

Abstract: In order to solve the problem that the Kernelized Correlation Filter (KCF) algorithm leads to target tracking failure due to the accumulation of measurement error, a sample quality evaluation mechanism is proposed to screen the sample to update the classifier. In order to solve the problem of repositioning after target occlusion, the Kalman filtering algorithm is used to estimate the target position, and then the estimation results are evaluated.In order to solve the problem that the target location is difficult to predict, the ORB feature point matching algorithm is used to complete the relocation of the target. A partial sequence in the TB dataset is selected for testing. Experimental results show that when the target appears in short-term and long-term occlusion, the improved algorithm improves the accuracy and success rate to a certain extent.

Key words: object tracking, kernelized correlation filter, sample updating, Kalman filter, ORB feature point

CLC Number:

TP391.4

WU Shi-yu， LI Zhi-hua， WANG Wei. A Method of Sample Updating and Target Repositioning Based on KCF[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2020.01.021.

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