💻 computer science

Omni Survey for Multimodality Analysis in Visual Object Tracking

This paper presents a comprehensive survey of multi-modal visual object tracking (MMVOT) that systematically covers data modalities, collection challenges, method categorization based on RGB and auxiliary X-branch integration, and evaluation benchmarks, while critically analyzing dataset biases and questioning the inherent superiority of multi-modal fusion over unimodal approaches.

Original authors: Zhangyong Tang, Tianyang Xu, Xuefeng Zhu, Hui Li, Shaochuan Zhao, Tao Zhou, Chunyang Cheng, Xiaojun Wu, Josef Kittler

Published 2026-03-18

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Zhangyong Tang, Tianyang Xu, Xuefeng Zhu, Hui Li, Shaochuan Zhao, Tao Zhou, Chunyang Cheng, Xiaojun Wu, Josef Kittler

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you are trying to find a lost friend in a crowded, chaotic city. If you only have your eyes (visible light), you might lose them if it gets dark, if it's foggy, or if they hide behind a wall. But what if you had a superpower? What if you could also see their body heat (thermal), hear their footsteps (sonar), feel the space around them (depth), or even read their mind (language)?

This paper is a massive "User's Manual" and "State of the Union" address for a field of AI called Multi-Modal Visual Object Tracking. In simple terms, it's about teaching computers to follow moving objects (like cars, people, or animals) by combining different types of "senses" instead of just relying on a standard camera.

Here is a breakdown of the paper's key ideas using everyday analogies:

1. The Problem: One Eye Isn't Enough

Standard computer vision is like trying to navigate a city with your eyes closed, relying only on a flashlight. It works great on a sunny day, but if it rains, gets dark, or you're underwater, the flashlight fails.

The Solution: The paper argues that we need to give our AI "super-senses."
- Thermal (Heat): Like seeing a warm cup of coffee in a cold room. It works in the dark.
- Depth (3D): Like having 3D glasses that tell you exactly how far away something is.
- Event (Motion): Like a camera that only takes pictures when something moves, ignoring the stillness. It's super fast.
- Language: Like having a friend whisper, "Look for the guy in the red hat," instead of just pointing at a blurry blob.

2. The Big Survey: A "Travel Guide" for AI

The authors didn't just build a new tracker; they wrote a comprehensive travel guide for the entire industry. They looked at 338 different research papers and organized them like a library.

The "Replicated" vs. "Custom" Analogy:
- Replicated: Imagine you have a chef who is amazing at cooking steak (RGB/Camera). You ask them to cook fish (Thermal) using the exact same knife and pan. It works okay, but maybe not perfectly.
- Custom: This paper suggests that for some "ingredients" (like heat or sound), you need a different chef with a different set of tools. You shouldn't force a thermal sensor to work exactly like a camera; it has its own unique physics.

3. The "Gotchas": Where Things Go Wrong

The paper highlights two major traps that researchers often fall into:

Trap #1: "More is Always Better" (The Fusion Fallacy)
- The Myth: "If I mix all my senses together, the AI will be perfect."
- The Reality: Imagine trying to drive a car while someone is screaming in your ear and the radio is blasting static. If one sense is broken (e.g., the camera is covered in mud), forcing the AI to "listen" to that broken sense can actually make it worse.
- The Lesson: The paper argues for Selective Fusion. The AI needs to be smart enough to say, "The camera is useless right now because it's dark; I'll ignore it and rely entirely on the thermal sensor."
Trap #2: The "Missing Animals" Problem
- The authors looked at all the data sets used to train these AIs and found a huge bias. Most data is about cars and people.
- The Analogy: It's like training a doctor only on human patients and then expecting them to treat a dog or a cat.
- The Issue: There is almost no data for tracking animals (like deer or birds) using these multi-sense tools. This is a big gap because tracking wildlife is a huge real-world use case.

4. The Future: What's Next?

The paper suggests three exciting directions for the future:

Smarter Sensors: Instead of just pasting different cameras together, we need to build systems that understand why a thermal camera sees things differently than a regular one.
Better Data: We need to go out and film more animals and more "bad weather" scenarios so the AI doesn't get confused when the real world gets messy.
The "All-in-One" Tracker: Currently, you might need one AI for thermal, another for depth, and another for language. The goal is to build one universal AI that can switch between these senses instantly, like a Swiss Army Knife.

Summary

Think of this paper as the foundation for a new generation of "Super-Spy" AI. It tells us that while combining different senses (seeing, feeling heat, hearing, reading text) is the key to making robots and self-driving cars truly smart, we have to be careful not to mix them blindly. We also need to stop training them only on humans and cars and start teaching them to see the whole world—including the animals and the storms.

The authors have even created a "Greatest Hits" playlist (a GitHub repository) of all the best code and tools so other researchers can start building these super-senses immediately.

1. Problem Statement

Visual Object Tracking (VOT) is a critical component for intelligent systems in smart cities, autonomous vehicles, and robotics. While traditional VOT relies on single-modal RGB data, it often fails in challenging conditions (e.g., low light, occlusion, adverse weather). Multi-modal Visual Object Tracking (MMVOT) addresses this by fusing RGB with auxiliary modalities (Thermal, Depth, Event, Language, Sonar, etc.).

However, the field faces several critical gaps:

Fragmented Literature: Existing surveys focus narrowly on specific pairs (e.g., only RGB+Thermal or RGB+Depth) rather than providing a holistic view of all modalities.
Indiscriminate Fusion: Current methods often assume that fusing all modalities at every frame is beneficial, ignoring scenarios where one modality is noisy or uninformative.
Data Bias: Existing MMVOT datasets exhibit severe long-tail distributions in object categories and a notable scarcity of animal data, hindering generalization.
Lack of Unified Frameworks: There is a disconnect between training (often on mixed data) and testing (on single-task benchmarks), and a lack of unified models that can handle multiple modalities simultaneously.

2. Methodology and Scope

The paper conducts a comprehensive "omni-survey" covering six distinct MMVOT tasks:

RGB + Thermal (T): For low-light and night tracking.
RGB + Depth (D): For 3D perception and occlusion handling.
RGB + Event (E): For high-speed motion and low-latency tracking.
RGB + Language (L): For semantic-based tracking (Text-to-Track).
RGB + Near-Infrared (NIR): For enhanced visibility in specific lighting.
RGB + Sonar (S): For underwater or foggy environments.

Key Technical Components Analyzed:

Data Modalities & Collection: The authors detail the physical principles of each sensor (e.g., ToF for Depth, asynchronous events for Event cameras) and the challenges of multi-sensor alignment and calibration.
Taxonomy of Methods: The survey classifies existing trackers based on how they handle the auxiliary modality ( $X$ $X$ ) relative to the RGB branch:
- Replicated Configuration: The $X$ branch mirrors the RGB architecture.
- Non-Replicated Configuration: The $X$ branch uses a custom architecture tailored to the modality's physics (e.g., Spiking Neural Networks for Event data, Text Encoders for Language).
- Unified Trackers: A new category of models (Uni-A and Uni-B) capable of handling multiple MMVOT tasks simultaneously, often leveraging pre-trained foundation models.
Fusion Strategies: The paper analyzes Early, Single-Level, and Multi-Level fusion, as well as temporal, frequency, and spatial-channel enhancement techniques.

3. Key Contributions

First Comprehensive Omni-Survey: This is the first work to cover all six major MMVOT tasks (RGB+T, D, E, L, NIR, S) in a single framework, encompassing 338 references.
Critical Rhetorical Question & Analysis: The authors challenge the prevailing assumption that "fusion is always better." Through quantitative analysis (Fig. 7), they demonstrate that indiscriminate fusion can degrade performance when one modality is low-quality. They advocate for discriminative fusion (selective fusion based on data quality).
Dataset Distribution Analysis: For the first time, the paper analyzes the object category distributions in MMVOT datasets. It reveals a pronounced long-tail nature and a critical lack of animal categories compared to RGB datasets, which limits the applicability of trackers in wildlife monitoring.
Unified Tracker Benchmarking: The paper introduces a detailed comparison of unified trackers (e.g., SUTrack, SeqTrackV2) and highlights that the most successful models often leverage RGB pre-training and replay mechanisms to maintain robustness across modalities.
Resource Curation: The authors provide a curated, up-to-date GitHub repository (Awesome-MultiModal-Visual-Object-Tracking) containing datasets, code, and benchmarks.

4. Results and Findings

Performance Trends: Unified trackers like SUTrack currently lead benchmarks (LasHeR, DepthTrack, VisEvent, TNL2K) by utilizing powerful backbones (e.g., Fast iTPN-L) and incorporating additional RGB data during training to preserve discriminative power.
The "Fusion Paradox": Experiments show that in specific scenarios (e.g., extreme darkness or motion blur), fusing a noisy modality with a clean one yields results comparable to random noise or worse than using the clean modality alone. This validates the need for quality-aware fusion.
Data Scarcity: The analysis of datasets (Fig. 9) confirms that while RGB datasets (like LaSOT) cover diverse categories, MMVOT datasets are heavily skewed toward humans and vehicles, with almost no representation of animals, despite the high demand for multi-modal animal tracking.
Efficiency vs. Accuracy: While complex fusion models improve accuracy, they often suffer from high computational costs. Recent trends show a shift toward lightweight architectures (e.g., Mamba-based, one-stream backbones) to balance real-time performance with accuracy.

5. Significance and Future Directions

This survey serves as a foundational resource for the MMVOT community, shifting the focus from simply "adding more modalities" to understanding the physical properties and quality of data.

Proposed Future Directions:

Discriminative Fusion Paradigms: Developing models that can autonomously decide when to fuse based on real-time data quality estimation.
Physics-Informed Architectures: Designing neural networks that explicitly incorporate the physical laws of specific sensors (e.g., thermal radiation decay, event sparsity) rather than treating all inputs as generic 3-channel images.
Diverse Data Collection: Urging the community to collect datasets in task-specific scenarios (e.g., night, underwater) and to include under-represented categories like animals.
Multi-Modal LLMs: Exploring the integration of Large Language Models into non-language MMVOT tasks to leverage their reasoning capabilities for robust tracking.
Better Evaluation Metrics: Moving beyond simple tracking accuracy to metrics that quantify the quality of information fusion itself.

In conclusion, the paper argues that the future of MMVOT lies not just in deeper networks, but in smarter, physics-aware, and selective fusion strategies supported by more diverse and representative datasets.