Dual Flow Perception Module

Dual-flow perception modules are designed to improve the accuracy and speed of real-time visual perception systems by incorporating both static and dynamic information from video streams. Current research focuses on integrating these modules into object detection and tracking algorithms, often employing novel loss functions and model architectures like transformers and normalizing flows to enhance performance in challenging scenarios such as those involving motion blur, fisheye distortion, and atmospheric turbulence. This approach is particularly significant for applications requiring immediate and accurate environmental understanding, such as autonomous driving and robotic navigation, where predicting future object states based on current motion is crucial for safety and efficiency.