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Appearance Adaptive Visual Tracking Algorithm Based on the KCF and SURF Features

The correlation filter-based trackers have demonstrated excellent performance for visual tracking according to the fast response in the frequency domain and the discriminant approach applied to it. However, it is difficult to maintain stable and robust tracking in such scenes like appearance deformation and occlusion. To address this issue, we employ an appearance adaptive algorithm based on the combination of SURF features and traditional KCF method in feature extraction, target localization strategy, and model updating strategy, to alleviate the algorithm accuracy reduction. Experiments show that our proposed method is competitive with other present algorithms and the average of distance precision becomes 6.7% higher than the KCF.