To track the 3D locations and trajectories of the other traffic participants at any given time, modern autonomous vehicles are equipped with multiple cameras that cover the vehicle's full surroundings.
One recent promising approach to the Visual Place Recognition (VPR) problem has been to fuse the place recognition estimates of multiple complementary VPR techniques using methods such as SRAL and multi-process fusion.
In this paper, we present Quasi-Dense Similarity Learning, which densely samples hundreds of object regions on a pair of images for contrastive learning.
Ranked #3 on Multiple Object Tracking on BDD100K
We evaluate this new scalable modular system on benchmark localization datasets Nordland and Oxford RobotCar, with comparisons to both standard techniques NetVLAD and SAD, and a previous spiking neural network system.
We provide empirical evidence for our claims through our ablations, demonstrating that the identified critical subset of layers is disproportionately powerful at detecting OOD samples in comparison to the rest of the network.
Event cameras continue to attract interest due to desirable characteristics such as high dynamic range, low latency, virtually no motion blur, and high energy efficiency.
We propose a point label aware method for propagating labels within superpixel regions to obtain augmented ground truth for training a semantic segmentation model.
We introduce powerful ideas from Hyperdimensional Computing into the challenging field of Out-of-Distribution (OOD) detection.
A recent approach to the Visual Place Recognition (VPR) problem has been to fuse the place recognition estimates of multiple complementary VPR techniques simultaneously.
Our approach uses Young tableaux for which a submatrix of the steering matrix has a vanishing determinant, which can be expressed through vanishing sums of unit roots.
Spiking neural networks (SNNs) offer both compelling potential advantages, including energy efficiency and low latencies and challenges including the non-differentiable nature of event spikes.
My overarching research goal is to provide robots with perceptional abilities that allow interactions with humans in a human-like manner.
Probabilistic state-estimation approaches offer a principled foundation for designing localization systems, because they naturally integrate sequences of imperfect motion and exteroceptive sensor data.
Experiments on our proposed simulation data and real-world benchmarks, including KITTI, nuScenes, and Waymo datasets, show that our tracking framework offers robust object association and tracking on urban-driving scenarios.
Ranked #6 on Multiple Object Tracking on KITTI Tracking test
Visual Place Recognition (VPR) is often characterized as being able to recognize the same place despite significant changes in appearance and viewpoint.
Visual Place Recognition is a challenging task for robotics and autonomous systems, which must deal with the twin problems of appearance and viewpoint change in an always changing world.
Ranked #1 on Visual Localization on RobotCar Seasons v2
A key challenge in visual place recognition (VPR) is recognizing places despite drastic visual appearance changes due to factors such as time of day, season, weather or lighting conditions.
The softening of the EoS by the PT at higher densities, i. e. after merging, leads to a characteristic increase of the dominant postmerger GW frequency f_peak relative to the tidal deformability Lambda inferred during the premerger inspiral phase.
High Energy Astrophysical Phenomena High Energy Physics - Phenomenology Nuclear Theory
Event cameras are bio-inspired sensors capable of providing a continuous stream of events with low latency and high dynamic range.
We further incorporate our proposed RT-BENE baselines in the recently presented RT-GENE gaze estimation framework where it provides a real-time inference of the openness of the eyes.
Ranked #1 on Blink estimation on RT-BENE
Object tracking and 3D reconstruction are often performed together, with tracking used as input for reconstruction.
We find that the axion emissivity is reduced by over an order of magnitude with respect to the basic OPE calculation, after all these effects are accounted for.
High Energy Physics - Phenomenology Solar and Stellar Astrophysics
We first record a novel dataset of varied gaze and head pose images in a natural environment, addressing the issue of ground truth annotation by measuring head pose using a motion capture system and eye gaze using mobile eyetracking glasses.
Ranked #1 on Gaze Estimation on RT-GENE
We propose a new context-aware correlation filter based tracking framework to achieve both high computational speed and state-of-the-art performance among real-time trackers.
Ranked #14 on Visual Object Tracking on VOT2017/18
We propose a new tracking framework with an attentional mechanism that chooses a subset of the associated correlation filters for increased robustness and computational efficiency.
1 code implementation • 12 Jun 2017 • Clément Moulin-Frier, Tobias Fischer, Maxime Petit, Grégoire Pointeau, Jordi-Ysard Puigbo, Ugo Pattacini, Sock Ching Low, Daniel Camilleri, Phuong Nguyen, Matej Hoffmann, Hyung Jin Chang, Martina Zambelli, Anne-Laure Mealier, Andreas Damianou, Giorgio Metta, Tony J. Prescott, Yiannis Demiris, Peter Ford Dominey, Paul F. M. J. Verschure
This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both the human and the robot.
In this paper, we present a novel framework for finding the kinematic structure correspondence between two objects in videos via hypergraph matching.
We revise the bound from the supernova SN1987A on the coupling of ultralight axion-like particles (ALPs) to photons.
High Energy Astrophysical Phenomena High Energy Physics - Phenomenology