  According to observations, different visual objects have different salient
features in different scenarios. Even for the same object, its salient shape
and appearance features may change greatly from time to time in a long-term
tracking task. Motivated by them, we proposed an end-to-end feature fusion
framework based on Siamese network, named FF-Siam, which can effectively fuse
different features for adaptive visual tracking. The framework consists of four
layers. A feature extraction layer is designed to extract the different
features of the target region and search region. The extracted features are
then put into a weight generation layer to obtain the channel weights, which
indicate the importance of different feature channels. Both features and the
channel weights are utilized in a template generation layer to generate a
discriminative template. Finally, the corresponding response maps created by
the convolution of the search region features and the template are applied with
a fusion layer to obtain the final response map for locating the target.
Experimental results demonstrate that the proposed framework achieves
state-of-the-art performance on the popular Temple-Color, OTB50 and UAV123
benchmarks.
