# Single Object Tracking: A Survey of Methods, Datasets, and Evaluation Metrics

arXiv: https://arxiv.org/abs/2201.13066 · HTML: https://mlsanity.com/paper/2201.13066/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 100 references that are in the map.
- First posted on arXiv 31 Jan 2022.

## Builds on (most cited first, 23 of 23)

- [Fully-Convolutional Siamese Networks for Object Tracking](https://mlsanity.com/paper/1606.09549/) (2016)
- [GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild](https://mlsanity.com/paper/1810.11981/) (2018)
- [LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking](https://mlsanity.com/paper/1809.07845/) (2018)
- [TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild](https://mlsanity.com/paper/1803.10794/) (2018)
- [ECO: Efficient Convolution Operators for Tracking](https://mlsanity.com/paper/1611.09224/) (2016)
- [Learning Multi-Domain Convolutional Neural Networks for Visual Tracking](https://mlsanity.com/paper/1510.07945/) (2015)
- [Need for Speed: A Benchmark for Higher Frame Rate Object Tracking](https://mlsanity.com/paper/1703.05884/) (2017)
- [YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video](https://mlsanity.com/paper/1702.00824/) (2017)
- [End-to-end representation learning for Correlation Filter based tracking](https://mlsanity.com/paper/1704.06036/) (2017)
- [Learning to Track at 100 FPS with Deep Regression Networks](https://mlsanity.com/paper/1604.01802/) (2016)
- [Siamese Instance Search for Tracking](https://mlsanity.com/paper/1605.05863/) (2016)
- [Long-term Tracking in the Wild: A Benchmark](https://mlsanity.com/paper/1803.09502/) (2018)
- [Video Object Segmentation and Tracking: A Survey](https://mlsanity.com/paper/1904.09172/) (2019)
- [Real-Time MDNet](https://mlsanity.com/paper/1808.08834/) (2018)
- [Visual object tracking performance measures revisited](https://mlsanity.com/paper/1502.05803/) (2015)
- [Modeling and Propagating CNNs in a Tree Structure for Visual Tracking](https://mlsanity.com/paper/1608.07242/) (2016)
- [Tracking Noisy Targets: A Review of Recent Object Tracking Approaches](https://mlsanity.com/paper/1802.03098/) (2018)
- [Understanding and Diagnosing Visual Tracking Systems](https://mlsanity.com/paper/1504.06055/) (2015)
- [Once for All: a Two-flow Convolutional Neural Network for Visual Tracking](https://mlsanity.com/paper/1604.07507/) (2016)
- [Gabor Wavelets in Image Processing](https://mlsanity.com/paper/1602.03308/) (2016)
- [Real-time visual tracking by deep reinforced decision making](https://mlsanity.com/paper/1702.06291/) (2017)
- [Optimized Object Tracking Technique Using Kalman Filter](https://mlsanity.com/paper/2103.05467/) (2021)
- [Faster and Simpler Siamese Network for Single Object Tracking](https://mlsanity.com/paper/2105.03049/) (2021)

## Built on by (most cited first, 5 of 5)

- [Visual Object Tracking across Diverse Data Modalities: A Review](https://mlsanity.com/paper/2412.09991/) (2024)
- [Absolute Evaluation Measures for Machine Learning: A Survey](https://mlsanity.com/paper/2507.03392/) (2025)
- [CARPE-ID: Continuously Adaptable Re-identification for Personalized Robot Assistance](https://mlsanity.com/paper/2310.19413/) (2023)
- [CSAOT: Cooperative Multi-Agent System for Active Object Tracking](https://mlsanity.com/paper/2501.13994/) (2025)
- [Robotic Contextual Awareness for Human-Robot Collaboration and Environmental Understanding](https://mlsanity.com/paper/2607.10372/) (2026)

## Abstract (arXiv)

Object tracking is one of the foremost assignments in computer vision that has numerous commonsense applications such as traffic monitoring, robotics, autonomous vehicle tracking, and so on. Different researches have been tried later a long time, but since of diverse challenges such as occlusion, illumination variations, fast motion, etc. researches in this area continues. In this paper, different strategies of the following objects are inspected and a comprehensive classification is displayed that classified the following strategies into four fundamental categories of feature-based, segmentation-based, estimation-based, and learning-based methods that each of which has its claim sub-categories. The most center of this paper is on learning-based strategies, which are classified into three categories of generative strategies, discriminative strategies, and reinforcement learning. One of the sub-categories of the discriminative show is deep learning. Since of high-performance, deep learning has as of late been exceptionally much consider. Finally, the different datasets and the evaluation methods that are most commonly used will be introduced.