  Visual tracking plays an important role in perception system, which is a
crucial part of intelligent transportation. Recently, Siamese network is a hot
topic for visual tracking to estimate moving targets' trajectory, due to its
superior accuracy and simple framework. In general, Siamese tracking
algorithms, supervised by logistic loss and triplet loss, increase the value of
inner product between exemplar template and positive sample while reduce the
value of inner product with background sample. However, the distractors from
different exemplars are not considered by mentioned loss functions, which limit
the feature models' discrimination. In this paper, a new exemplar loss
integrated with logistic loss is proposed to enhance the feature model's
discrimination by reducing inner products among exemplars. Without the bells
and whistles, the proposed algorithm outperforms the methods supervised by
logistic loss or triplet loss. Numerical results suggest that the newly
developed algorithm achieves comparable performance in public benchmarks.
