The event camera is a biomimetic dynamic vision sensor with advantages such as high time resolution, wide dynamic response range, and low power consumption. It can continuously capture changes in light intensity within the field of view. Developing visual measurement solutions based on event cameras is crucial for addressing dynamic problems. However, event-based measurement systems face two significant challenges. Firstly, event cameras output asynchronous event streams, which lose spatial information during transmission, making it difficult to reference traditional vision measurement algorithms. Secondly, event cameras lack reliable filtering algorithms, leading to poor-quality restored event frames, which are insufficient for reliably calculating image features. Our study outlines the development process of event cameras and reviews research on event-based target tracking. We also discuss advancements in event camera calibration, event-based structured light measurement, and event-based autofocus techniques. The 3D measurement scheme based on event cameras encounters issues with the unreliability of event stream data features and low measurement accuracy. By studying spatio-temporal information extraction algorithms, we aim to improve measurement accuracy. Developing high-speed event camera measurement systems and designing efficient solutions with low-bandwidth, low-power, and small computation will further advance the field of high-speed visual measurement.