Multi-Filter Support Correlation Filters Tracking Algorithm
In the support correlation filter tracking method,calculations are converted into frequency domain by cyclic sampling,which elimi-nates less sampling and high computational complexity of support vector machine(SVM).However,the current method linearly interpolates historical and current samples during the tracking process to obtain training samples,which cannot effectively utilize the historical information of the samples.In response to this issue,this paper proposes a multi filter supported correlation filtering tracking method.During the tracking process,first train the historical filter using historical samples,and then use the historical filter to constrain the current filter,which can bet-ter utilize the historical information of the samples.Experiments on the OTB100 database showed that the algorithm achieved an accuracy of 79.2%and a success rate of 58.6%.Compared to the Scale Kernel Support Correlation Filtering algorithm(SKSCF),the algorithm proposed in this paper improved accuracy and success rate respectively by 2%and 3.7%.
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