8 papers
Realizing Fully-Integrated, Low-Power, Event-Based Pupil Tracking with Neuromorphic Hardware
Federico Paredes-Valles, Yoshitaka Miyatani, Kirk Y. W. Scheper
Eye tracking is fundamental to numerous applications, yet achieving robust, high-frequency tracking with ultra-low power consumption remains challenging for wearable platforms. Whi…
EV-LayerSegNet: Self-supervised Motion Segmentation using Event Cameras
Youssef Farah, Federico Paredes-Vallés, Guido De Croon +3
Event cameras are novel bio-inspired sensors that capture motion dynamics with much higher temporal resolution than traditional cameras, since pixels react asynchronously to bright…
RGB-Event Fusion with Self-Attention for Collision Prediction
Pietro Bonazzi, Christian Vogt, Michael Jost +4
Ensuring robust and real-time obstacle avoidance is critical for the safe operation of autonomous robots in dynamic, real-world environments. This paper proposes a neural network f…
Towards Low-Latency Event-based Obstacle Avoidance on a FPGA-Drone
Pietro Bonazzi, Christian Vogt, Michael Jost +3
This work quantitatively evaluates the performance of event-based vision systems (EVS) against conventional RGB-based models for action prediction in collision avoidance on an FPGA…
From Soft Materials to Controllers with NeuroTouch: A Neuromorphic Tactile Sensor for Real-Time Gesture Recognition
Victor Hoffmann, Federico Paredes-Valles, Valentina Cavinato
This work presents NeuroTouch, an optical-based tactile sensor that combines a highly deformable dome-shaped soft material with an integrated neuromorphic camera, leveraging frame-…
On-Device Self-Supervised Learning of Low-Latency Monocular Depth from Only Events
Jesse Hagenaars, Yilun Wu, Federico Paredes-Vallés +2
Event cameras provide low-latency perception for only milliwatts of power. This makes them highly suitable for resource-restricted, agile robots such as small flying drones. Self-s…