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Alexandre Massoud Alahi

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Related publications (121)

Page 4 of 7

Sparsity Driven People Localization with a Heterogeneous Network of Cameras

Pierre Vandergheynst, Alexandre Massoud Alahi, Laurent Jacques

In this paper, we propose to study the problem of localization of a dense set of people with a network of heterogeneous cameras. We propose to recast the problem as a linear inverse problem. The proposed framework is generic to any scene, scalable in the n ...

Springer Verlag2011

Urban Digital Twin Challenges: A Systematic Review and Perspectives for Sustainable Smart Cities

Alexandre Massoud Alahi, François Golay, Simon Elias Bibri, Charlotte Gisèle Weil, Régis Longchamp

Recent scientific and technological advancements have transformed the knowledge frontiers, giving rise to the next wave of disruptive technologies with deep impacts on urban society. An Urban Digital Twin (UDT) is a technology with great potential to trans ...

ELSEVIER2023

MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimation

Alexandre Massoud Alahi, Sven Kreiss, Lorenzo Bertoni

We tackle the fundamentally ill-posed problem of 3D human localization from monocular RGB images. Driven by the limitation of neural networks outputting point estimates, we address the ambiguity in the task by predicting confidence intervals through a loss ...

2019

Towards more Practical Threat Models in Artificial Intelligence Security

Alexandre Massoud Alahi, Kathrin Grosse

Recent works have identified a gap between research and practice in artificial intelligence security: threats studied in academia do not always reflect the practical use and security risks of AI. For example, while models are often studied in isolation, th ...

Usenix Association2024

Toward Reliable Human Pose Forecasting With Uncertainty

Alexandre Massoud Alahi, Saeed Saadatnejad, Taylor Ferdinand Mordan

Recently, there has been an arms race of pose forecasting methods aimed at solving the spatio-temporal task of predicting a sequence of future 3D poses of a person given a sequence of past observed ones. However, the lack of unified benchmarks and limited ...

2024

Social-Transmotion: Promptable Human Trajectory Prediction

Alexandre Massoud Alahi, Saeed Saadatnejad, Yang Gao, Kaouther Messaoud Ben Amor

Accurate human trajectory prediction is crucial for applications such as autonomous vehicles, robotics, and surveillance systems. Yet, existing models often fail to fully leverage the non-verbal social cues human subconsciously communicate when navigating ...

2024

TAROT: Targeted Data Selection via Optimal Transport

Lan Feng, Alexandre Massoud Alahi, Yuejiang Liu

We propose TAROT, a Targeted data selection framework grounded in Optimal Transport theory. Previous targeted data selection methods primarily use influencebased greedy heuristics to enhance domain-specific performance. These methods perform well on limite ...

2025

Eye-Rubbing Detection Using a Smartwatch: A Feasibility Study Demonstrated High Accuracy With Machine Learning

Alexandre Massoud Alahi, Tom Louis Mery

Purpose: In this work, we present a new machine learning method based on the transformer neural network to detect eye rubbing using a smartwatch in a real-life setting. In ophthalmology, the accurate detection and prevention of eye rubbing could reduce inc ...

2024

Towards Motion Forecasting with Real-World Perception Inputs: Are End-to-End Approaches Competitive?

Alexandre Massoud Alahi

Motion forecasting is crucial in enabling autonomous vehicles to anticipate the future trajectories of surrounding agents. To do so, it requires solving mapping, detection, tracking, and then forecasting problems, in a multi-step pipeline. In this complex ...

2024

Detecting 32 Pedestrian Attributes for Autonomous Vehicles

Alexandre Massoud Alahi, Taylor Ferdinand Mordan

Pedestrians are arguably one of the most safety-critical road users to consider for autonomous vehicles in urban areas. In this paper, we address the problem of jointly detecting pedestrians and recognizing 32 pedestrian attributes. These encompass visual ...

IEEE2020

Deep Visual Re-Identification with Confidence

Alexandre Massoud Alahi, Sven Kreiss, George Adaimi

Transportation systems often rely on understanding the flow of vehicles or pedestrian. From traffic monitoring at the city scale, to commuters in train terminals, recent progress in sensing technology make it possible to use cameras to better understand th ...

2019

Proxy methods for detection of inhalation exposure in simulated office environments

Alexandre Massoud Alahi, Dusan Licina, Seoyeon Yun

BACKGROUND: Modern health concerns related to air pollutant exposure in buildings have been exacerbated owing to several factors. Methods for assessing inhalation exposures indoors have been restricted to stationary air pollution measurements, typically as ...

SPRINGERNATURE2022

Deep visual Re-identification with confidence

Alexandre Massoud Alahi, Sven Kreiss, George Adaimi

Transportation systems often rely on understanding the flow of vehicles or pedestrian. Fromtraffic monitoring at the city scale, to commuters in train terminals, recent progress in sensingtechnology make it possible to use cameras to better understand the ...

2021

Motion Style Transfer: Modular Low-Rank Adaptation for Deep Motion Forecasting

Alexandre Massoud Alahi, Yuejiang Liu, Parth Ashit Kothari, Danya Li

Deep motion forecasting models have achieved great success when trained on a massive amount of data. Yet, they often perform poorly when training data is limited. To address this challenge, we propose a transfer learning approach for efficiently adapting p ...

2022

TTT++: When Does Self-Supervised Test-Time Training Fail or Thrive?

Bastien Germain C. Van Delft, Alexandre Massoud Alahi, Yuejiang Liu, Taylor Ferdinand Mordan, Parth Ashit Kothari

Test-time training (TTT) through self-supervised learning (SSL) is an emerging paradigm to tackle distributional shifts. Despite encouraging results, it remains unclear when this approach thrives or fails. In this work, we first provide an in-depth look at ...

2021

Learning nuisances to track pedestrians in autonomous vehicles

Alexandre Massoud Alahi, George Adaimi

Autonomous vehicles rely on an accurate perception module. One of the fundamental challenges is to efficiently track pedestrians surrounding a vehicle to anticipate risky situations. Over the past decades, researchers have formulated the tracking problem a ...

2018

SoccerNet 2022 Challenges Results

Alexandre Massoud Alahi, Vladimir Dominic K. Somers

The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team. In 2022, the challenges were composed of 6 vision-based tasks: (1) action spotting, focusing on retrieving action timestamps in long untrim ...

Assoc Computing Machinery2022

Convolutional Relational Machine for Group Activity Recognition

Alexandre Massoud Alahi

We present an end-to-end deep Convolutional Neural Network called Convolutional Relational Machine (CRM) for recognizing group activities that utilizes the information in spatial relationsbetween individualpersons in image or video. It learns to produce an ...

IEEE2019

System and method for media library navigation and recommendation

Pierre Vandergheynst, Alexandre Massoud Alahi, Kirell Maël Benzi

The invention relates to a system and method to optimize navigation and recommendation in a distributed network of media libraries. According to the invention, it is proposed a system and a method to assist the selection of media items comprising the steps ...

2014

System and Method for Measuring Performances of Surveillance Systems

Alexandre Massoud Alahi

A computer implemented method measures a performance of surveillance system. A site model, a sensor model and a traffic model are selected respectively from a set of site models, a set of sensor models, and a set of traffic models to form a surveillance mo ...

2008