Event Based

Event-based systems leverage asynchronous data streams, capturing changes rather than continuous frames, offering advantages in speed, energy efficiency, and dynamic range. Current research focuses on applying this paradigm to various computer vision tasks, including object recognition, SLAM (Simultaneous Localization and Mapping), and low-light image enhancement, often employing neural networks, transformers, and spiking neural networks tailored for event data processing. This approach holds significant promise for improving the robustness and efficiency of computer vision systems in resource-constrained environments and challenging scenarios, impacting fields like robotics, autonomous driving, and neuromorphic computing.