In the realm of video annotation for machine learning, action classifications, also known as dynamic or event-based classifications, offer a more nuanced approach than static annotations by focusing on the actions and movements of objects over time. This method enhances data richness for computer vision models by allowing annotators to label the specific activities of dynamic objects, such as cars accelerating or turning, thus contributing to a more accurate ground truth. Despite its benefits, implementing dynamic classification is challenging due to its complexity and the limited availability of tools that support it, like Encord. These classifications are crucial for sectors that rely on movement-based video data, such as autonomous driving and sports analytics, because they enable the creation of highly detailed training datasets that improve the performance and accuracy of machine learning models. The process requires meticulous attention to detail and data quality, as well as a comprehensive understanding of the dynamic properties the annotations aim to capture, making it an essential yet demanding component in the development of computer vision technologies.