# A Twofold Siamese Network for Real-Time Object Tracking

## TL;DR

This paper introduces SA-Siam, a real-time object tracking network combining semantic and appearance features through separate training and a channel attention mechanism, significantly enhancing tracking accuracy.

## Contribution

The paper proposes a novel twofold Siamese network with separate training for semantic and appearance branches, incorporating a channel attention mechanism for improved real-time tracking.

## Key findings

- Outperforms all other real-time trackers on OTB benchmarks
- Significantly improves tracking performance with the twofold design
- Channel attention mechanism enhances feature relevance

## Abstract

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. In addition, we propose a channel attention mechanism for the semantic branch. Channel-wise weights are computed according to the channel activations around the target position. While the inherited architecture from SiamFC \cite{SiamFC} allows our tracker to operate beyond real-time, the twofold design and the attention mechanism significantly improve the tracking performance. The proposed SA-Siam outperforms all other real-time trackers by a large margin on OTB-2013/50/100 benchmarks.