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Davi Frossard

Davi Frossard
0 BLOG ARTICLES 2 RESEARCH PAPERS
I'm a research scientist at Uber ATG Toronto, developing cutting edge computer vision technology for self driving vehicles. Concurrently, I'm a PhD student at the University of Toronto doing research on computer vision under the supervision of prof. Raquel Urtasun. My research interests lie in the intersection of classic computational geometry, vision and machine learning. Previously, I did my Master's at the University of Toronto (also with prof. Urtasun) with the thesis on extracting visual vehicle attributes from a self driving platform and my Bachelor's degree at the Federal University of Espirito Santo (UFES) in Computer Engineering, writing my thesis on end-to-end learning of multiple object tracking.

Research Papers

DeepSignals: Predicting Intent of Drivers Through Visual Attributes

D. Frossard, E. Kee, R. Urtasun
Detecting the intention of drivers is an essential task in self-driving, necessary to anticipate sudden events like lane changes and stops. Turn signals and emergency flashers communicate such intentions, providing seconds of potentially critical reaction time. In this paper, we propose to detect these signals in video sequences by using a deep neural network that reasons about both spatial and temporal information. [...] [PDF]
International Conference on Robotics and Automation (ICRA), 2019

End-to-end Learning of Multi-sensor 3D Tracking by Detection

D. Frossard, R. Urtasun
In this paper we propose a novel approach to tracking by detection that can exploit both cameras as well as LIDAR data to produce very accurate 3D trajectories. Towards this goal, we formulate the problem as a linear program that can be solved exactly, and learn convolutional networks for detection as well as matching in an end-to-end manner. [...] [PDF]
International Conference on Robotics and Automation (ICRA), 2018

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