# Tracking Noisy Targets: A Review of Recent Object Tracking Approaches

arXiv: https://arxiv.org/abs/1802.03098 · HTML: https://mlsanity.com/paper/1802.03098/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2018.

## Built on by (most cited first, 6 of 6)

- [Tracking Without Re-recognition in Humans and Machines](https://mlsanity.com/paper/2105.13351/) (2021)
- [Single Object Tracking: A Survey of Methods, Datasets, and Evaluation Metrics](https://mlsanity.com/paper/2201.13066/) (2022)
- [The Challenge of Appearance-Free Object Tracking with Feedforward Neural Networks](https://mlsanity.com/paper/2110.02772/) (2021)
- [CheckSoft : A Scalable Event-Driven Software Architecture for Keeping Track of People and Things in People-Centric Spaces](https://mlsanity.com/paper/2102.10513/) (2021)
- [Detecting Biological Locomotion in Video: A Computational Approach](https://mlsanity.com/paper/2105.12661/) (2021)
- [Trajectory Prediction in Dynamic Object Tracking: A Critical Study](https://mlsanity.com/paper/2506.19341/) (2025)

## Abstract (arXiv)

Visual object tracking is an important computer vision problem with numerous real-world applications including human-computer interaction, autonomous vehicles, robotics, motion-based recognition, video indexing, surveillance and security. In this paper, we aim to extensively review the latest trends and advances in the tracking algorithms and evaluate the robustness of trackers in the presence of noise. The first part of this work comprises a comprehensive survey of recently proposed tracking algorithms. We broadly categorize trackers into correlation filter based trackers and the others as non-correlation filter trackers. Each category is further classified into various types of trackers based on the architecture of the tracking mechanism. In the second part of this work, we experimentally evaluate tracking algorithms for robustness in the presence of additive white Gaussian noise. Multiple levels of additive noise are added to the Object Tracking Benchmark (OTB) 2015, and the precision and success rates of the tracking algorithms are evaluated. Some algorithms suffered more performance degradation than others, which brings to light a previously unexplored aspect of the tracking algorithms. The relative rank of the algorithms based on their performance on benchmark datasets may change in the presence of noise. Our study concludes that no single tracker is able to achieve the same efficiency in the presence of noise as under noise-free conditions; thus, there is a need to include a parameter for robustness to noise when evaluating newly proposed tracking algorithms.