# SiamDCFF: Dynamic Cascade Feature Fusion for Vision Tracking

## TL;DR

This paper introduces SiamDCFF, a new tracking method that improves feature fusion in Siamese networks by enhancing global dependencies.

## Contribution

The novel dynamic cascade feature fusion module improves global dependency modeling in Siamese network-based trackers.

## Key findings

- The DCFF module significantly enhances the performance of fully convolutional trackers.
- SiamDCFF outperforms the baseline model on public tracking datasets.
- Global dependencies in feature fusion lead to more robust tracking results.

## Abstract

Establishing an accurate and robust feature fusion mechanism is key to enhancing the tracking performance of single-object trackers based on a Siamese network. However, the output features of the depth-wise cross-correlation feature fusion module in fully convolutional trackers based on Siamese networks cannot establish global dependencies on the feature maps of a search area. This paper proposes a dynamic cascade feature fusion (DCFF) module by introducing a local feature guidance (LFG) module and dynamic attention modules (DAMs) after the depth-wise cross-correlation module to enhance the global dependency modeling capability during the feature fusion process. In this paper, a set of verification experiments is designed to investigate whether establishing global dependencies for the features output by the depth-wise cross-correlation operation can significantly improve the performance of fully convolutional trackers based on a Siamese network, providing experimental support for rational design of the structure of a dynamic cascade feature fusion module. Secondly, we integrate the dynamic cascade feature fusion module into the tracking framework based on a Siamese network, propose SiamDCFF, and evaluate it using public datasets. Compared with the baseline model, SiamDCFF demonstrated significant improvements.