Summary

In this chapter, we explored the world of video data classification, its real-world applications, and various methods for labeling and classifying video data. We discussed techniques such as frame-based classification, 3D CNNs, auto encoders, transfer learning, and Watershed methods. Additionally, we examined the latest advances in video data labeling, including self-supervised learning, transformer-based models, GNNs, weakly supervised learning, domain adaptation, few-shot learning, and active learning. These advancements contribute to more accurate, efficient, and scalable video data labeling and classification systems, enabling breakthroughs in domains such as surveillance, healthcare, sports analysis, autonomous driving, and social media. By keeping up with the latest developments and leveraging these techniques, researchers and practitioners can unlock the full potential of video data and derive valuable insights from this rich and dynamic information source.

In the...