# Dense Feature Aggregation and Pruning for RGBT Tracking

## TL;DR

This paper introduces a novel deep fusion and pruning method for RGBT tracking that densely aggregates multi-layer features and prunes redundant information, leading to improved tracking performance.

## Contribution

It proposes a recursive dense feature aggregation strategy and a collaborative feature pruning method for enhanced RGBT tracking.

## Key findings

- Achieves state-of-the-art results on RGBT benchmarks.
- Effectively removes redundant features through pruning.
- Improves robustness of target representations.

## Abstract

How to perform effective information fusion of different modalities is a core factor in boosting the performance of RGBT tracking. This paper presents a novel deep fusion algorithm based on the representations from an end-to-end trained convolutional neural network. To deploy the complementarity of features of all layers, we propose a recursive strategy to densely aggregate these features that yield robust representations of target objects in each modality. In different modalities, we propose to prune the densely aggregated features of all modalities in a collaborative way. In a specific, we employ the operations of global average pooling and weighted random selection to perform channel scoring and selection, which could remove redundant and noisy features to achieve more robust feature representation. Experimental results on two RGBT tracking benchmark datasets suggest that our tracker achieves clear state-of-the-art against other RGB and RGBT tracking methods.