Publications associées à Détection de personnes

Improving Object Detection under Domain Shifts

Object detection plays a critical role in various computer vision applications, encompassing domains like autonomous vehicles, object tracking, and scene understanding. These applica- tions rely on detectors that generate bounding boxes around known object ...

EPFL2023

Detecting Anomalies and Obstacles in Road Scenes

Krzysztof Maciej Lis

We address the problem of segmenting anomalies and unusual obstacles in road scenes for the purpose of self-driving safety. The objects in question are not present in the common training sets as it is not feasible to collect and annotate examples for every ...

EPFL2023

Reshaping Perception for Autonomous Driving with Semantic Keypoints

Lorenzo Bertoni

The field of artificial intelligence is set to fuel the future of mobility by driving forward the transition from advanced driver-assist systems to fully autonomous vehicles (AV). Yet the current technology, backed by cutting-edge deep learning techniques, ...

EPFL2022

Radatron: Accurate Detection Using Multi-resolution Cascaded MIMO Radar

Haitham Al Hassanieh, Junfeng Guan, Seyedsohrab Madani, Saurabh Gupta, Waleed Ahmed

Millimeter wave (mmWave) radars are becoming a more popular sensing modality in self-driving cars due to their favorable characteristics in adverse weather. Yet, they currently lack sufficient spatial resolution for semantic scene understanding. In this pa ...

SPRINGER INTERNATIONAL PUBLISHING AG2022

Vision-based Drone Flocking in Outdoor Environments

Dario Floreano, Fabrizio Schiano, Fabian Maximilian Schilling

Decentralized deployment of drone swarms usually relies on inter-agent communication or visual markers that are mounted on the vehicles to simplify their mutual detection. This letter proposes a vision-based detection and tracking algorithm that enables gr ...

2021

Pedestrian intention prediction: A convolutional bottom-up multi-task approach

Alexandre Massoud Alahi, Taylor Ferdinand Mordan

The ability to predict pedestrian behaviour is crucial for road safety, traffic management systems, Advanced Driver Assistance Systems (ADAS), and more broadly autonomous vehicles. We present a vision-based system that simultaneously locates where pedestri ...

2021

Deep Generative Models and Applications

Tatjana Chavdarova

Over the past few years, there have been fundamental breakthroughs in core problems in machine learning, largely driven by advances in deep neural networks. The amount of annotated data drastically increased and supervised deep discriminative models exceed ...

EPFL2020

Exploring the relative efficacy of motion artefact correction techniques for EEG data acquired during simultaneous fMRI

João Pedro Forjaco Jorge

Simultaneous EEG-fMRI allows multiparametric characterisation of brain function, in principle enabling a more complete understanding of brain responses; unfortunately the hostile MRI environment severely reduces EEG data quality. Simply eliminating data se ...

Wiley2019

Detecting the Unexpected via Image Resynthesis

Pascal Fua, Mathieu Salzmann, Krishna Kanth Nakka, Krzysztof Maciej Lis

Classical semantic segmentation methods, including the recent deep learning ones, assume that all classes observed at test time have been seen during training. In this paper, we tackle the more realistic scenario where unexpected objects of unknown classes ...

IEEE2019

Evaluating and Interpreting Deep Convolutional Neural Networks via Non-negative Matrix Factorization

Edo Collins

With ever greater computational resources and more accessible software, deep neural networks have become ubiquitous across industry and academia. Their remarkable ability to generalize to new samples defies the conventional view, which holds that complex, ...

EPFL2019

Reparameterizing Discontinuous Integrands for Differentiable Rendering

Wenzel Alban Jakob, Guillaume Rémi Bruno Loubet

Differentiable rendering has recently opened the door to a number of challenging inverse problems involving photorealistic images, such as computational material design and scattering-aware reconstruction of geometry and materials from photographs. Differe ...

ASSOC COMPUTING MACHINERY2019

Variational Methods for Human Modeling

Timur Bagautdinov

A large part of computer vision research is devoted to building models and algorithms aimed at understanding human appearance and behaviour from images and videos. Ultimately, we want to build automated systems that are at least as capable as people when i ...

EPFL2018

Target Detection with Deep Learning in Polarimetric Imaging

Selman Ergünay, Yusuf Leblebici, Süha Kagan Köse

Polarimetric imaging techniques demonstrate enhanced capabilities in advanced object detection tasks with their capability to discriminate man-made objects from natural background surfaces. While spectral signatures carry information only about material pr ...

SPIE-INT SOC OPTICAL ENGINEERING2018

Mean-Field methods for Structured Deep-Learning in Computer Vision

Pierre Bruno Baqué

In recent years, Machine Learning based Computer Vision techniques made impressive progress. These algorithms proved particularly efficient for image classification or detection of isolated objects. From a probabilistic perspective, these methods can predi ...

EPFL2018

MnO2 nanosheets as an artificial enzyme to mimic oxidase for rapid and sensitive detection of glutathione

Paul Joseph Dyson, Zhaofu Fei, Jing Liu

Nanozymes are increasingly used as components in assays and diagnostics. Here, we describe a rapid and highly sensitive colorimetric assay for the detection and quantification of glutathione (GSH) employing MnO2 nanosheets as an artificial oxidase. In the ...

Elsevier2017

Vision-based detection of aircrafts and UAVs

Artem Rozantsev

Unmanned Aerial Vehicles are becoming increasingly popular for a broad variety of tasks ranging from aerial imagery to objects delivery. With the expansion of the areas, where drones can be efficiently used, the collision risk with other flying objects inc ...

EPFL2017

Vision-Based Unmanned Aerial Vehicle Detection and Tracking for Sense and Avoid Systems

Pascal Fua, Denis Gillet, Alcherio Martinoli, Vincent Lepetit, Artem Rozantsev, Steven Adriaan Roelofsen, Krishna Raj Sapkota

We propose an approach for on-line detection of small Unmanned Aerial Vehicles (UAVs) and estimation of their relative positions and velocities in the 3D environment from a single moving camera in the context of sense and avoid systems. This problem is cha ...

Ieee2016

Putting low-level vision into global context: Why vision cannot be reduced to basic circuits

Michael Herzog, Yvonne Evelina Thunell

To cope with the complexity of vision, most models in neuroscience and computer vision are of hierarchical and feedforward nature. Low-level vision, such as edge and motion detection, is explained by basic low-level neural circuits, whose outputs serve as ...

Elsevier2016

Robust 3D Object Pose Estimation and Tracking from Monocular Images in Industrial Environments

Alberto Crivellaro

Recent advances in Computer Vision are changing our way of living and enabling new applications for both leisure and professional use. Regrettably, in many industrial domains the spread of state-of-the-art technologies is made challenging by the abundance ...

EPFL2016

Tracking Interacting Objects in Image Sequences

Xinchao Wang

Object tracking in image sequences is a key challenge in computer vision. Its goal is to follow objects that move or evolve over time while preserving the identity of each object. However, most existing approaches focus on one class of objects and model on ...

EPFL2015