EANet: Enhanced Attribute-based RGBT Tracker Network

TL;DR

This paper introduces EANet, a deep learning-based RGBT tracker that fuses RGB and thermal images, improving tracking performance under challenging conditions like occlusion and lighting changes.

Contribution

The paper presents a novel attribute-specific feature fusion method within an enhanced architecture for RGBT tracking, outperforming existing methods with fewer parameters.

Findings

Outperforms state-of-the-art RGBT trackers on RGBT234 and LasHeR datasets.

Uses attribute-specific feature selection and aggregation for improved accuracy.

Achieves better robustness in challenging tracking scenarios.

Abstract

Tracking objects can be a difficult task in computer vision, especially when faced with challenges such as occlusion, changes in lighting, and motion blur. Recent advances in deep learning have shown promise in challenging these conditions. However, most deep learning-based object trackers only use visible band (RGB) images. Thermal infrared electromagnetic waves (TIR) can provide additional information about an object, including its temperature, when faced with challenging conditions. We propose a deep learning-based image tracking approach that fuses RGB and thermal images (RGBT). The proposed model consists of two main components: a feature extractor and a tracker. The feature extractor encodes deep features from both the RGB and the TIR images. The tracker then uses these features to track the object using an enhanced attribute-based architecture. We propose a fusion of attribute-specific feature selection with an aggregation module. The proposed methods are evaluated on the RGBT234 \cite{LiCLiang2018} and LasHeR \cite{LiLasher2021} datasets, which are the most widely used RGBT object-tracking datasets in the literature. The results show that the proposed system outperforms state-of-the-art RGBT object trackers on these datasets, with a relatively smaller number of parameters.