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Efficient Processing of Event Data for Deep Learning
This project investigates new paradigms for low-level event data processing (from event cameras) to enable expressive and efficient feature extraction.
Keywords: Event Cameras, Deep Learning, Machine Learning
Event Cameras show great potential for high-speed and low-latency computer vision and robotics applications. In recent years, we have particularly focused on designing novel data-driven approaches to maximize performance and minimize latency. Still, efficient and effective feature extraction from event data remains challenging. This project investigates novel approaches proposed in the machine-learning and signal-processing community to maximize the benefits of event cameras in time-critical scenarios. Applications of this project range include optical flow prediction, object detection, and tracking to closed-loop control with event-based vision in the loop. Contact us for more details.
Event Cameras show great potential for high-speed and low-latency computer vision and robotics applications. In recent years, we have particularly focused on designing novel data-driven approaches to maximize performance and minimize latency. Still, efficient and effective feature extraction from event data remains challenging. This project investigates novel approaches proposed in the machine-learning and signal-processing community to maximize the benefits of event cameras in time-critical scenarios. Applications of this project range include optical flow prediction, object detection, and tracking to closed-loop control with event-based vision in the loop. Contact us for more details.
This project aims to develop a new differentiable module for efficient and effective low-level event data processing. To achieve this goal, we will investigate new techniques recently proposed in the machine learning and signal processing literature. We will integrate the proposed method with state-of-the-art deep learning models to enable low-latency inference in applications ranging from optical flow prediction, object detection/tracking, and closed-loop drone navigation with vision in the loop.
This project aims to develop a new differentiable module for efficient and effective low-level event data processing. To achieve this goal, we will investigate new techniques recently proposed in the machine learning and signal processing literature. We will integrate the proposed method with state-of-the-art deep learning models to enable low-latency inference in applications ranging from optical flow prediction, object detection/tracking, and closed-loop drone navigation with vision in the loop.