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

TL;DR

This paper reviews object tracking methods, emphasizing their evolution from classical to deep learning approaches, and discusses their applications and challenges in biomedical research for understanding cellular processes.

Contribution

It categorizes and analyzes various object tracking techniques with a focus on biomedical applications, highlighting current limitations and future research directions.

Findings

Deep learning improves tracking accuracy in complex environments

Traditional methods are limited in high-density, occluded settings

Emerging trends aim to develop more robust biomedical tracking systems

Abstract

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.