# A Survey of Appearance Models in Visual Object Tracking

arXiv: https://arxiv.org/abs/1303.4803 · HTML: https://mlsanity.com/paper/1303.4803/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2013.

## Built on by (most cited first, 21 of 21)

- [VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flows](https://mlsanity.com/paper/2108.05015/) (2021)
- [Towards More Flexible and Accurate Object Tracking with Natural Language: Algorithms and Benchmark](https://mlsanity.com/paper/2103.16746/) (2021)
- [Learn to Match: Automatic Matching Network Design for Visual Tracking](https://mlsanity.com/paper/2108.00803/) (2021)
- [Visual Object Tracking with Discriminative Filters and Siamese Networks: A Survey and Outlook](https://mlsanity.com/paper/2112.02838/) (2021)
- [Anti-UAV: A Large Multi-Modal Benchmark for UAV Tracking](https://mlsanity.com/paper/2101.08466/) (2021)
- [Learning Target-aware Representation for Visual Tracking via Informative Interactions](https://mlsanity.com/paper/2201.02526/) (2022)
- [Unified Sequence-to-Sequence Learning for Single- and Multi-Modal Visual Object Tracking](https://mlsanity.com/paper/2304.14394/) (2023)
- [Learning to Filter: Siamese Relation Network for Robust Tracking](https://mlsanity.com/paper/2104.00829/) (2021)
- [FOLT: Fast Multiple Object Tracking from UAV-captured Videos Based on Optical Flow](https://mlsanity.com/paper/2308.07207/) (2023)
- [Siamese Object Tracking for Unmanned Aerial Vehicle: A Review and Comprehensive Analysis](https://mlsanity.com/paper/2205.04281/) (2022)
- [Transformers in Single Object Tracking: An Experimental Survey](https://mlsanity.com/paper/2302.11867/) (2023)
- [ETAP: Event-based Tracking of Any Point](https://mlsanity.com/paper/2412.00133/) (2024)
- [Distractor-aware Event-based Tracking](https://mlsanity.com/paper/2310.14194/) (2023)
- [Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-based Beam Search](https://mlsanity.com/paper/2205.09676/) (2022)
- [MM-Tracker: Motion Mamba with Margin Loss for UAV-platform Multiple Object Tracking](https://mlsanity.com/paper/2407.10485/) (2024)
- [Tracklet-Switch Adversarial Attack against Pedestrian Multi-Object Tracking Trackers](https://mlsanity.com/paper/2111.08954/) (2021)
- [Continuity-Discrimination Convolutional Neural Network for Visual Object Tracking](https://mlsanity.com/paper/2104.08739/) (2021)
- [Pyramid Correlation based Deep Hough Voting for Visual Object Tracking](https://mlsanity.com/paper/2110.07994/) (2021)
- [Multi-target tracking for video surveillance using deep affinity network: a brief review](https://mlsanity.com/paper/2110.15674/) (2021)
- [New Performance Measures for Object Tracking under Complex Environments](https://mlsanity.com/paper/2111.07145/) (2021)
- [T-SiamTPN: Temporal Siamese Transformer Pyramid Networks for Robust and Efficient UAV Tracking](https://mlsanity.com/paper/2509.12913/) (2025)

## Abstract (arXiv)

Visual object tracking is a significant computer vision task which can be applied to many domains such as visual surveillance, human computer interaction, and video compression. In the literature, researchers have proposed a variety of 2D appearance models. To help readers swiftly learn the recent advances in 2D appearance models for visual object tracking, we contribute this survey, which provides a detailed review of the existing 2D appearance models. In particular, this survey takes a module-based architecture that enables readers to easily grasp the key points of visual object tracking. In this survey, we first decompose the problem of appearance modeling into two different processing stages: visual representation and statistical modeling. Then, different 2D appearance models are categorized and discussed with respect to their composition modules. Finally, we address several issues of interest as well as the remaining challenges for future research on this topic. The contributions of this survey are four-fold. First, we review the literature of visual representations according to their feature-construction mechanisms (i.e., local and global). Second, the existing statistical modeling schemes for tracking-by-detection are reviewed according to their model-construction mechanisms: generative, discriminative, and hybrid generative-discriminative. Third, each type of visual representations or statistical modeling techniques is analyzed and discussed from a theoretical or practical viewpoint. Fourth, the existing benchmark resources (e.g., source code and video datasets) are examined in this survey.