Event cameras are novel visual sensors inspired by biology, representing an interdisciplinary research hotspot in computational neuroscience and computer vision. Unlike traditional cameras, event cameras can asynchronously output event streams related to brightness changes, offering advantages such as high time resolution, wide dynamic range, low latency, low bandwidth, and low power consumption. They are suitable for real-time dynamic perception of high-speed moving targets and represent a new research direction in computer vision detection and tracking. This review first introduces the types, working principles, advantages, and disadvantages of event cameras, followed by an in-depth analysis of existing object detection and tracking algorithms based on event cameras. Subsequently, event datasets related to visual detection and tracking are introduced. Finally, future development trends in this field are discussed.