  Visual tracking is one of the most challenging computer vision problems. In
order to achieve high performance visual tracking in various negative
scenarios, a novel cascaded Siamese network is proposed and developed based on
two different deep learning networks: a matching subnetwork and a
classification subnetwork. The matching subnetwork is a fully convolutional
Siamese network. According to the similarity score between the exemplar image
and the candidate image, it aims to search possible object positions and crop
scaled candidate patches. The classification subnetwork is designed to further
evaluate the cropped candidate patches and determine the optimal tracking
results based on the classification score. The matching subnetwork is trained
offline and fixed online, while the classification subnetwork performs
stochastic gradient descent online to learn more target-specific information.
To improve the tracking performance further, an effective classification
subnetwork update method based on both similarity and classification scores is
utilized for updating the classification subnetwork. Extensive experimental
results demonstrate that our proposed approach achieves state-of-the-art
performance in recent benchmarks.
