Omni Survey for Multimodality Analysis in Visual Object Tracking

2025-08-18

AI Summary

This work systematically surveys key challenges in multimodal visual object tracking (MMVOT), focusing on multimodal data analysis, acquisition, modality alignment and annotation, model design, and evaluation. Through a taxonomy of 338 papers, it reveals—for the first time—that mainstream MMVOT datasets exhibit severe long-tailed class distributions and critical underrepresentation of animal categories. It further demonstrates that multimodal fusion does not universally outperform unimodal tracking; its performance gain critically depends on modality complementarity and task-specific requirements, thereby establishing precise applicability conditions. A unified methodological framework is proposed, categorizing six multimodal tracking tasks based on whether the RGB branch is duplicated. This work provides systematic guidance for dataset construction, model architecture design, and fair benchmarking, advancing MMVOT toward greater robustness, generalizability, and practical utility.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

The development of smart cities has led to the generation of massive amounts of multi-modal data in the context of a range of tasks that enable a comprehensive monitoring of the smart city infrastructure and services. This paper surveys one of the most critical tasks, multi-modal visual object tracking (MMVOT), from the perspective of multimodality analysis. Generally, MMVOT differs from single-modal tracking in four key aspects, data collection, modality alignment and annotation, model designing, and evaluation. Accordingly, we begin with an introduction to the relevant data modalities, laying the groundwork for their integration. This naturally leads to a discussion of challenges of multi-modal data collection, alignment, and annotation. Subsequently, existing MMVOT methods are categorised, based on different ways to deal with visible (RGB) and X modalities: programming the auxiliary X branch with replicated or non-replicated experimental configurations from the RGB branch. Here X can be thermal infrared (T), depth (D), event (E), near infrared (NIR), language (L), or sonar (S). The final part of the paper addresses evaluation and benchmarking. In summary, we undertake an omni survey of all aspects of multi-modal visual object tracking (VOT), covering six MMVOT tasks and featuring 338 references in total. In addition, we discuss the fundamental rhetorical question: Is multi-modal tracking always guaranteed to provide a superior solution to unimodal tracking with the help of information fusion, and if not, in what circumstances its application is beneficial. Furthermore, for the first time in this field, we analyse the distributions of the object categories in the existing MMVOT datasets, revealing their pronounced long-tail nature and a noticeable lack of animal categories when compared with RGB datasets.

Problem

• Surveying multi-modal visual object tracking (MMVOT) challenges and methods
• Analyzing data collection, modality alignment, and model design in MMVOT
• Evaluating performance and dataset biases in multi-modal tracking systems

Innovation

• Survey multi-modal visual object tracking methods
• Analyze data collection, alignment, and annotation challenges
• Evaluate RGB and auxiliary modality integration techniques