Abstract
In machine learning, training sample set management has an important impact on the performance of visual detection and tracking algorithms, as corrupted training samples degrade the tracking performance, especially in practical scenarios such as vehicular networks. However, how to evaluate and remove the corrupted training samples still remains a challenging topic. In this paper, we propose a novel scheme to remove the corrupted training samples in visual tracking, which will improve the tracking performance dramatically. In the proposed scheme, a novel training sample set management method based on the adaptive sample weight is presented. Specifically, similarity learning is first utilized to evaluate the quality of training samples with similarity score. Then, if the similarity score is below a certain threshold, the training sample is deemed as the corrupted one and is removed from the training sample set. The experimental results show that the proposed scheme obtains superior performances on visual tracking benchmarks and vehicular scenarios.
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