# Learning Deep Multi-Level Similarity for Thermal Infrared Object Tracking

arXiv: https://arxiv.org/abs/1906.03568 · HTML: https://mlsanity.com/paper/1906.03568/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2019.

## Built on by (most cited first, 19 of 19)

- [Single-Model and Any-Modality for Video Object Tracking](https://mlsanity.com/paper/2311.15851/) (2023)
- [Anti-UAV: A Large Multi-Modal Benchmark for UAV Tracking](https://mlsanity.com/paper/2101.08466/) (2021)
- [Exploring Fusion Strategies for Accurate RGBT Visual Object Tracking](https://mlsanity.com/paper/2201.08673/) (2022)
- [Active Learning for Deep Visual Tracking](https://mlsanity.com/paper/2110.13259/) (2021)
- [Thermal3D-GS: Physics-induced 3D Gaussians for Thermal Infrared Novel-view Synthesis](https://mlsanity.com/paper/2409.08042/) (2024)
- [Learning Localization-aware Target Confidence for Siamese Visual Tracking](https://mlsanity.com/paper/2204.14093/) (2022)
- [Unsupervised Cross-Modal Distillation for Thermal Infrared Tracking](https://mlsanity.com/paper/2108.00187/) (2021)
- [In Defense and Revival of Bayesian Filtering for Thermal Infrared Object Tracking](https://mlsanity.com/paper/2402.17098/) (2024)
- [Context-aware Visual Tracking with Joint Meta-updating](https://mlsanity.com/paper/2204.01513/) (2022)
- [Progressive Domain Adaptation for Thermal Infrared Object Tracking](https://mlsanity.com/paper/2407.19430/) (2024)
- [Data Generation Scheme for Thermal Modality with Edge-Guided Adversarial Conditional Diffusion Model](https://mlsanity.com/paper/2408.03748/) (2024)
- [SMMT: Siamese Motion Mamba with Self-attention for Thermal Infrared Target Tracking](https://mlsanity.com/paper/2505.04088/) (2025)
- [Self-Supervised RGB-T Tracking with Cross-Input Consistency](https://mlsanity.com/paper/2301.11274/) (2023)
- [Searching a Lightweight Network Architecture for Thermal Infrared Pedestrian Tracking](https://mlsanity.com/paper/2402.16570/) (2024)
- [Coordinate-Aware Thermal Infrared Tracking Via Natural Language Modeling](https://mlsanity.com/paper/2407.08265/) (2024)
- [Performance Evaluation of Deep Learning-based Quadrotor UAV Detection and Tracking Methods](https://mlsanity.com/paper/2410.00285/) (2024)
- [FGSGT: Saliency-Guided Siamese Network Tracker Based on Key Fine-Grained Feature Information for Thermal Infrared Target Tracking](https://mlsanity.com/paper/2504.14309/) (2025)
- [DCFG: Diverse Cross-Channel Fine-Grained Feature Learning and Progressive Fusion Siamese Tracker for Thermal Infrared Target Tracking](https://mlsanity.com/paper/2504.14311/) (2025)
- [Infrared UAV Target Tracking with Dynamic Feature Refinement and Global Contextual Attention Knowledge Distillation](https://mlsanity.com/paper/2512.04581/) (2025)

## Abstract (arXiv)

Existing deep Thermal InfraRed (TIR) trackers only use semantic features to describe the TIR object, which lack the sufficient discriminative capacity for handling distractors. This becomes worse when the feature extraction network is only trained on RGB images.To address this issue, we propose a multi-level similarity model under a Siamese framework for robust TIR object tracking. Specifically, we compute different pattern similarities on two convolutional layers using the proposed multi-level similarity network. One of them focuses on the global semantic similarity and the other computes the local structural similarity of the TIR object. These two similarities complement each other and hence enhance the discriminative capacity of the network for handling distractors. In addition, we design a simple while effective relative entropy based ensemble subnetwork to integrate the semantic and structural similarities. This subnetwork can adaptive learn the weights of the semantic and structural similarities at the training stage. To further enhance the discriminative capacity of the tracker, we construct the first large scale TIR video sequence dataset for training the proposed model. The proposed TIR dataset not only benefits the training for TIR tracking but also can be applied to numerous TIR vision tasks. Extensive experimental results on the VOT-TIR2015 and VOT-TIR2017 benchmarks demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.