Motion Perception

Motion perception research focuses on understanding how humans and machines perceive movement, aiming to replicate and improve upon biological visual processing capabilities. Current research emphasizes developing sophisticated computational models, including deep convolutional neural networks and recurrent self-attention networks, to accurately estimate and interpret motion cues from various sources like video and radar data, often incorporating techniques like model predictive control for real-time applications. These advancements have implications for improving artificial intelligence, particularly in robotics and autonomous systems, as well as enhancing the realism and safety of driving simulators and other virtual environments. Furthermore, insights gained are informing our understanding of the neural mechanisms underlying human motion perception.