Visual tracking systems are essential for applications ranging from surveillance to autonomous navigation. However, these systems have a significant Achilles’ heel: they rely heavily on large, labeled datasets for training. This reliance makes it challenging to deploy them in real-world situations where labeled data is scarce or expensive to obtain. In this article, we will learn about self-supervised learning (SSL) and how it leverages unlabeled data to train models.
What Is the Problem?
Visual tracking involves identifying and following an object across frames in a video. Traditional methods depend on vast amounts of labeled data to learn how to recognize and track objects accurately. This dependence poses several problems: