A Deep Dive into Generic Object Tracking: A Survey

2025-07-31

AI Summary

General object tracking remains challenging due to occlusion, appearance variation, and similar distractors. This paper systematically surveys three mainstream paradigms—Siamese, discriminative, and Transformer-based approaches—with particular emphasis on the emerging Transformer-based paradigm. We propose a novel taxonomy tailored for general-purpose trackers and establish a unified, visualization-enhanced, structured comparison framework, analyzing methods across model architecture, spatiotemporal modeling mechanisms, and training strategies. Quantitative evaluation is conducted on major benchmarks including LaSOT and TrackingNet. Results demonstrate that Transformer-based trackers achieve superior robustness and generalization, primarily owing to their global contextual modeling and long-range dependency capture capabilities. The survey clarifies the technical evolution trajectory of general tracking, identifies current bottlenecks—including computational overhead, data hunger, and domain adaptability—and highlights promising future directions such as lightweight design, few-shot adaptation, and cross-domain transfer learning.

Technology Category

• Computer Vision: Motion & Tracking
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Security and Privacy: Tracking, profiling, and countermeasures against them
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Generic object tracking remains an important yet challenging task in computer vision due to complex spatio-temporal dynamics, especially in the presence of occlusions, similar distractors, and appearance variations. Over the past two decades, a wide range of tracking paradigms, including Siamese-based trackers, discriminative trackers, and, more recently, prominent transformer-based approaches, have been introduced to address these challenges. While a few existing survey papers in this field have either concentrated on a single category or widely covered multiple ones to capture progress, our paper presents a comprehensive review of all three categories, with particular emphasis on the rapidly evolving transformer-based methods. We analyze the core design principles, innovations, and limitations of each approach through both qualitative and quantitative comparisons. Our study introduces a novel categorization and offers a unified visual and tabular comparison of representative methods. Additionally, we organize existing trackers from multiple perspectives and summarize the major evaluation benchmarks, highlighting the fast-paced advancements in transformer-based tracking driven by their robust spatio-temporal modeling capabilities.

Problem

• Surveying generic object tracking challenges in computer vision
• Comparing tracking paradigms including transformer-based methods
• Analyzing design principles and benchmarks for tracking advancements

Innovation

• Comprehensive review of three tracking paradigms
• Emphasis on transformer-based tracking methods
• Unified visual and tabular comparison framework