# Object Tracking in a 360o View: A Novel Perspective on Bridging the Gap to Biomedical Advancements

arXiv: https://arxiv.org/abs/2412.01119 · HTML: https://mlsanity.com/paper/2412.01119/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 290 references that are in the map.
- First posted on arXiv 2 Dec 2024.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [Big Self-Supervised Models are Strong Semi-Supervised Learners](https://mlsanity.com/paper/2006.10029/) (2020)
- [An Introduction to Deep Reinforcement Learning](https://mlsanity.com/paper/1811.12560/) (2018)
- [SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory](https://mlsanity.com/paper/2411.11922/) (2024)
- [Improving the Improved Training of Wasserstein GANs: A Consistency Term and Its Dual Effect](https://mlsanity.com/paper/1803.01541/) (2018)
- [Multiview RGB-D Dataset for Object Instance Detection](https://mlsanity.com/paper/1609.07826/) (2016)
- [Deep Continuous Clustering](https://mlsanity.com/paper/1803.01449/) (2018)
- [Unsupervised Learning for Cell-level Visual Representation in Histopathology Images with Generative Adversarial Networks](https://mlsanity.com/paper/1711.11317/) (2017)
- [Microscopy Cell Segmentation via Convolutional LSTM Networks](https://mlsanity.com/paper/1805.11247/) (2018)
- [Transferring Rich Feature Hierarchies for Robust Visual Tracking](https://mlsanity.com/paper/1501.04587/) (2015)
- [Deep Reinforcement Learning for Visual Object Tracking in Videos](https://mlsanity.com/paper/1701.08936/) (2017)
- [Graph Neural Networks for 3D Multi-Object Tracking](https://mlsanity.com/paper/2008.09506/) (2020)
- [Tracking Multiple Moving Objects Using Unscented Kalman Filtering Techniques](https://mlsanity.com/paper/1802.01235/) (2018)
- [Use HMM and KNN for classifying corneal data](https://mlsanity.com/paper/1401.7486/) (2014)
- [Globally Optimal Cell Tracking using Integer Programming](https://mlsanity.com/paper/1501.05499/) (2015)
- [Computational Motility Tracking of Calcium Dynamics in Toxoplasma gondii](https://mlsanity.com/paper/1708.01871/) (2017)
- [Generative Adversarial Networks for Unsupervised Object Co-localization](https://mlsanity.com/paper/1806.00236/) (2018)

## Abstract (arXiv)

Object tracking is a fundamental tool in modern innovation, with applications in defense systems, autonomous vehicles, and biomedical research. It enables precise identification, monitoring, and spatiotemporal analysis of objects across sequential frames, providing insights into dynamic behaviors. In cell biology, object tracking is vital for uncovering cellular mechanisms, such as migration, interactions, and responses to drugs or pathogens. These insights drive breakthroughs in understanding disease progression and therapeutic interventions. Over time, object tracking methods have evolved from traditional feature-based approaches to advanced machine learning and deep learning frameworks. While classical methods are reliable in controlled settings, they struggle in complex environments with occlusions, variable lighting, and high object density. Deep learning models address these challenges by delivering greater accuracy, adaptability, and robustness. This review categorizes object tracking techniques into traditional, statistical, feature-based, and machine learning paradigms, with a focus on biomedical applications. These methods are essential for tracking cells and subcellular structures, advancing our understanding of health and disease. Key performance metrics, including accuracy, efficiency, and adaptability, are discussed. The paper explores limitations of current methods and highlights emerging trends to guide the development of next-generation tracking systems for biomedical research and broader scientific domains.