no code implementations • 20 Feb 2024 • Ignacio Roldan, Andras Palffy, Julian F. P. Kooij, Dariu M. Gavrila, Francesco Fioranelli, Alexander Yarovoy
In this paper, we address the limitations of traditional constant false alarm rate (CFAR) target detectors in automotive radars, particularly in complex urban environments with multiple objects that appear as extended targets.
no code implementations • 16 Oct 2023 • Mathijs R. van Geerenstein, Felicia Ruppel, Klaus Dietmayer, Dariu M. Gavrila
In experiments, we outperform the state of the art in transformer-based LiDAR object detection on the competitive nuScenes benchmark and showcase the benefits of input-dependent multimodal query initialization, while being more efficient than the available alternatives for LiDAR-camera initialization.
1 code implementation • CVPR 2023 • Fangqiang Ding, Andras Palffy, Dariu M. Gavrila, Chris Xiaoxuan Lu
This work proposes a novel approach to 4D radar-based scene flow estimation via cross-modal learning.
no code implementations • 23 Nov 2022 • Thomas M. Hehn, Julian F. P. Kooij, Dariu M. Gavrila
Various state-of-the-art self-supervised visual representation learning approaches take advantage of data from multiple sensors by aligning the feature representations across views and/or modalities.
no code implementations • 23 Nov 2022 • Philip de Rijk, Lukas Schneider, Marius Cordts, Dariu M. Gavrila
Knowledge Distillation (KD) is a well-known training paradigm in deep neural networks where knowledge acquired by a large teacher model is transferred to a small student.
no code implementations • 18 Nov 2020 • Christoph B. Rist, David Emmerichs, Markus Enzweiler, Dariu M. Gavrila
We show that this continuous representation is suitable to encode geometric and semantic properties of extensive outdoor scenes without the need for spatial discretization (thus avoiding the trade-off between level of scene detail and the scene extent that can be covered).
Ranked #5 on 3D Semantic Scene Completion on SemanticKITTI
no code implementations • 27 Apr 2020 • Christian Muench, Frans A. Oliehoek, Dariu M. Gavrila
Traffic scenarios are inherently interactive.
1 code implementation • 25 Apr 2020 • Andras Palffy, Jiaao Dong, Julian F. P. Kooij, Dariu M. Gavrila
In experiments on a real-life dataset we demonstrate that our method outperforms the state-of-the-art methods both target- and object-wise by reaching an average of 0. 70 (baseline: 0. 68) target-wise and 0. 56 (baseline: 0. 48) object-wise F1 score.
no code implementations • 15 May 2019 • Andrey Rudenko, Luigi Palmieri, Michael Herman, Kris M. Kitani, Dariu M. Gavrila, Kai O. Arras
With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important.
no code implementations • CVPR 2019 • Ries Uittenbogaard, Clint Sebastian, Julien Vijverberg, Bas Boom, Dariu M. Gavrila, Peter H. N. de With
The current paradigm in privacy protection in street-view images is to detect and blur sensitive information.
no code implementations • 18 May 2018 • Markus Braun, Sebastian Krebs, Fabian Flohr, Dariu M. Gavrila
The dataset furthermore contains a large number of person orientation annotations (over 211200).
no code implementations • Computer Vision and Image Understanding 2013 • Martijn C. Liem, Dariu M. Gavrila
We present a system to track the positions of multiple persons in a scene from overlapping cameras.