no code implementations • 11 Oct 2019 • Hazem Rashed, Mohamed Ramzy, Victor Vaquero, Ahmad El Sallab, Ganesh Sistu, Senthil Yogamani
In this work, we propose a robust and real-time CNN architecture for Moving Object Detection (MOD) under low-light conditions by capturing motion information from both camera and LiDAR sensors.
no code implementations • 8 Oct 2019 • Victor Vaquero, Kai Fischer, Francesc Moreno-Noguer, Alberto Sanfeliu, Stefan Milz
Results show that we are able to accurately re-locate over a filtered map, consistently reducing trajectory errors between an average of 35. 1% with respect to a non-filtered map version and of 47. 9% with respect to a standalone map created on the current session.
no code implementations • 30 Aug 2018 • Victor Vaquero, Alberto Sanfeliu, Francesc Moreno-Noguer
In this paper we propose a novel approach to estimate dense optical flow from sparse lidar data acquired on an autonomous vehicle.
no code implementations • 28 Aug 2018 • Victor Vaquero, Alberto Sanfeliu, Francesc Moreno-Noguer
Perception technologies in Autonomous Driving are experiencing their golden age due to the advances in Deep Learning.
no code implementations • 23 Aug 2018 • Victor Vaquero, Ivan del Pino, Francesc Moreno-Noguer, Joan Solà, Alberto Sanfeliu, Juan Andrade-Cetto
The system is thoroughly evaluated on the KITTI tracking dataset, and we show the performance boost provided by our CNN-based vehicle detector over a standard geometric approach.
no code implementations • 22 Aug 2018 • Victor Vaquero, German Ros, Francesc Moreno-Noguer, Antonio M. Lopez, Alberto Sanfeliu
We propose a novel representation for dense pixel-wise estimation tasks using CNNs that boosts accuracy and reduces training time, by explicitly exploiting joint coarse-and-fine reasoning.