1 code implementation • 27 Oct 2023 • Tianran Liu, Zeping Zhang, Morteza Mousa Pasandi, Robert Laganiere
Considering that our approach focuses only on the visible part of the foreground objects to achieve accurate 3D detection, we named our method What You See Is What You Detect (WYSIWYD).
1 code implementation • 29 Mar 2023 • James Giroux, Martin Bouchard, Robert Laganiere
Object detection utilizing Frequency Modulated Continous Wave radar is becoming increasingly popular in the field of autonomous systems.
1 code implementation • 2 May 2021 • Ao Zhang, Farzan Erlik Nowruzi, Robert Laganiere
In this paper, we collect a novel radar dataset that contains radar data in the form of Range-Azimuth-Doppler tensors along with the bounding boxes on the tensor for dynamic road users, category labels, and 2D bounding boxes on the Cartesian Bird-Eye-View range map.
no code implementations • 5 Mar 2021 • Farzan Erlik Nowruzi, Dhanvin Kolhatkar, Prince Kapoor, Robert Laganiere
Processing point clouds using deep neural networks is still a challenging task.
no code implementations • 4 Mar 2021 • Farzan Erlik Nowruzi, Dhanvin Kolhatkar, Prince Kapoor, Elnaz Jahani Heravi, Fahed Al Hassanat, Robert Laganiere, Julien Rebut, Waqas Malik
In this paper, we propose PolarNet, a deep neural model to process radar information in polar domain for open space segmentation.
no code implementations • 18 Mar 2020 • Farzan Erlik Nowruzi, Dhanvin Kolhatkar, Prince Kapoor, Fahed Al Hassanat, Elnaz Jahani Heravi, Robert Laganiere, Julien Rebut, Waqas Malik
In this work, we propose the use of radar with advanced deep segmentation models to identify open space in parking scenarios.
no code implementations • 16 Jul 2019 • Farzan Erlik Nowruzi, Prince Kapoor, Dhanvin Kolhatkar, Fahed Al Hassanat, Robert Laganiere, Julien Rebut
In this paper, we take a comprehensive look into the effects of replacing real data with synthetic data.
no code implementations • 23 Apr 2018 • Ding Lu, Yong Wang, Robert Laganiere, Xinbin Luo, Shan Fu
In this paper, we present a multi-scale Fully Convolutional Networks (MSP-RFCN) to robustly detect and classify human hands under various challenging conditions.
no code implementations • 3 Dec 2014 • Feng Shi, Robert Laganiere, Emil Petriu
This paper introduces a high efficient local spatiotemporal descriptor, called gradient boundary histograms (GBH).
no code implementations • CVPR 2013 • Feng Shi, Emil Petriu, Robert Laganiere
We present a real-time action recognition system which integrates fast random sampling method with local spatio-temporal features extracted from a Local Part Model.