67 citations · 67 across the 2 of their papers we have counts for
5 papers
RADDet: Range-Azimuth-Doppler based Radar Object Detection for Dynamic Road Users
Ao Zhang, Farzan Erlik Nowruzi, Robert Laganiere
Object detection using automotive radars has not been explored with deep learning models in comparison to the camera based approaches. This can be attributed to the lack of public…
Point Cloud based Hierarchical Deep Odometry Estimation
Farzan Erlik Nowruzi, Dhanvin Kolhatkar, Prince Kapoor +1
Processing point clouds using deep neural networks is still a challenging task. Most existing models focus on object detection and registration with deep neural networks using poin…
PolarNet: Accelerated Deep Open Space Segmentation Using Automotive Radar in Polar Domain
Farzan Erlik Nowruzi, Dhanvin Kolhatkar, Prince Kapoor +5
Camera and Lidar processing have been revolutionized with the rapid development of deep learning model architectures. Automotive radar is one of the crucial elements of automated d…
Deep Open Space Segmentation using Automotive Radar
Farzan Erlik Nowruzi, Dhanvin Kolhatkar, Prince Kapoor +5
In this work, we propose the use of radar with advanced deep segmentation models to identify open space in parking scenarios. A publically available dataset of radar observations c…
How much real data do we actually need: Analyzing object detection performance using synthetic and real data
Farzan Erlik Nowruzi, Prince Kapoor, Dhanvin Kolhatkar +3
In recent years, deep learning models have resulted in a huge amount of progress in various areas, including computer vision. By nature, the supervised training of deep models requ…