Gaze-Vector Estimation in the Dark with Temporally Encoded Event-driven Neural Networks

arXiv:2403.02909v1 Announce Type: new
Abstract: In this paper, we address the intricate challenge of gaze vector prediction, a pivotal task with applications ranging from human-computer interaction to driver monitoring systems. Our innovative approach is designed for the demanding setting of extremely low-light conditions, leveraging a novel temporal event encoding scheme, and a dedicated neural network architecture. The temporal encoding method seamlessly integrates Dynamic Vision Sensor (DVS) events with grayscale guide frames, generating consecutively encoded images for input into our neural …

abstract applications arxiv challenge computer cs.cv cs.hc driver eess.iv encoding event human human-computer interaction light low monitoring networks neural networks novel paper pivotal prediction systems temporal type vector