siamFC_mfc实现平移的代码

To investigate the underlying reason, we analyze the

Siamese network architecture and identify that the receptive

field size of neurons, network stride and feature padding

are three important factors affecting tracking accuracy. In

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1particular, the receptive field determines the image region

used in computing a feature. A larger receptive field pro-

vides greater image context, while a small one may not

capture the structure of target objects. The network stride

affects the degree of localization precision, especially for

small-sized objects. Meanwhile, it controls the size of out-

put feature maps, which affects feature discriminability and

detection accuracy. Moreover, for a fully-convolutional ar-

chitecture [2], the feature padding for convolutions induces

a potential position bias in model training, such that when

an object moves near the search range boundary, it has a

very low probability of being predicted as the target. These

three factors together prevent Siamese trackers from bene-

fiting from current deeper and more sophisticated network

architectures.