A Twofold Siamese Network for Real-Time Object Tracking

Understand

Observing that Semantic features learned in an image classification task and Appearance features learned in a similarity matching task complement each other, we build a twofold Siamese network, named SA-Siam, for real-time object tracking.

• SA-Siam is composed of a semantic branch and an appearance branch.
• Each branch is a similarity-learning Siamese network.
• An important design choice in SA-Siam is to separately train the two branches to keep the heterogeneity of the two types of features.