The Evolution of Object Tracking: From Classical Methods to Vision-Language Models

Author(s): Arpita Vats

Originally published on Towards AI.

Object tracking lies at the heart of modern computer vision, powering applications like autonomous driving, augmented reality, robotics, video surveillance, and sports analytics. From handcrafted filters to multimodal, promptable trackers guided by natural language, the field has undergone remarkable transformation in just two decades.

In this article, we’ll explore how tracking has evolved, the innovations driving recent progress, and what the future holds.

The article details the evolution of object tracking, highlighting key phases such as historical foundations, the rise of deep learning for single-object tracking, the complexities introduced by multi-object tracking, long-term tracking challenges, and the latest trends related to foundation and vision-language models that enhance adaptability and interactivity in tracking systems.

Read the full blog for free on Medium.

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Published via Towards AI