Robust visual tracking by embedding combination and weighted-gradient optimization

Highlights：

• We propose a negative sample embedding combination network specific for handling the imbalance between positive and negative samples in the tracking-by-detection framework.

• We propose a weighted-gradient loss to handle the imbalance between easy and hard samples by balancing the total gradient contributions of them.

• We conduct extensive experiments on tracking benchmarks. The results demonstrate that the proposed algorithm improves the performance of the baseline and performs favorably against state-of-the-art trackers.