# A Deep Dive into Generic Object Tracking: A Survey

arXiv: https://arxiv.org/abs/2507.23251 · HTML: https://mlsanity.com/paper/2507.23251/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2025.

## Built on by (most cited first, 2 of 2)

- [AR2-4FV: Anchored Referring and Re-identification for Long-Term Grounding in Fixed-View Videos](https://mlsanity.com/paper/2603.07758/) (2026)
- [Template-Search Domain Adaptation via Multi-Stage Feature Alignment for Cross-Modal Object Tracking](https://mlsanity.com/paper/2609.38637/) (2026)

## Abstract (arXiv)

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.