This paper introduces Helios, the first extremely low-power, real-time, event-based hand gesture recognition system designed for all-day on smart eyewear. Helios can recognize seven classes of gestures, including subtle microgestures like swipes and pinches, with 91% accuracy. We also demonstrate real-time performance across 20 users at a remarkably low latency of 60ms.
In this paper, we present SEVD, a first-of-its-kind multi-view ego, and fixed perception synthetic event-based dataset using multiple dynamic vision sensors within the CARLA simulator. We evaluate the dataset using state-of-the-art event-based (RED, RVT) and frame-based (YOLOv8) methods for traffic participant detection tasks and provide baseline benchmarks for assessment.
Prophesee et Bpifrance investissent 15 millions d’euros pour le développement d’une nouvelle génération de capteurs neuromorphique, fabriqués en France, destinés à l’IA embarquée dans 1,3 milliard de smartphones.
In this work, we present our exploration of optimizing event-based neural network inference on SENECA, a scalable and flexible neuromorphic architecture. Our optimizations for event-based neural networks can be potentially generalized to a wide range of event-based neuromorphic processors.
This research is the first to investigate the impact of bias modifications on the event-based DMS output and propose an approach for evaluating and comparing DMS performance. The study investigates the impact of pixel-bias alteration on DMS features, which are: face tracking, blink counting, head pose and gaze estimation. The results indicate that the DMS’s functioning is enhanced with proper bias tuning based on the proposed metrics.