Publications by Alexandre Massoud Alahi

Related publications (121)

Dynamic Voxels Based on Ego-Conditioned Prediction: An Integrated Spatio-Temporal Framework for Motion Planning

Prediction is a vital component of motion planning for autonomous vehicles (AVs). By reasoning about the possible behavior of other target agents, the ego vehicle (EV) can navigate safely, efficiently, and politely. However, most of the existing work overl ...

IEEE Institute of Electrical and Electronics Engineers2024

X-ray Micro-CT based characterization of rock cuttings with deep learning

Nils Frédérik Heiden Olsen, Alexandre Massoud Alahi, Pascal Turberg

Rock cuttings from destructive boreholes are a common and cheaper source of drilling materials that can be used to determine underground geology compared to rock core samples. Classifying manually the series of cuttings can be a long and tedious process an ...

2025

Adaptive hot water production based on Supervised Learning

Alexandre Massoud Alahi, Dolaana Khovalyg, Amirreza Heidari

A major challenge in the common approach of hot water generation in residential houses lies in the highly stochastic nature of domestic hot water (DHW) demand. Learning hot water use behavior enables water heating systems to continuously adapt to the stoch ...

2021

Sim-to-Real Causal Transfer: A Metric Learning Approach to Causally-Aware Interaction Representations

Alexandre Massoud Alahi, Yuejiang Liu, Po-Chien Luan, Ahmad Rahimi

Modeling spatial-temporal interactions among neighboring agents is at the heart of multi-agent problems such as motion forecasting and crowd navigation. Despite notable progress, it remains unclear to which extent modern representations can capture the cau ...

2025

Towards Generalizable Trajectory Prediction using Dual-Level Representation Learning and Adaptive Prompting

Alexandre Massoud Alahi, Kaouther Messaoud Ben Amor

Existing vehicle trajectory prediction models struggle with generalizability, prediction uncertainties, and handling complex interactions. It is often due to limitations like complex architectures customized for a specific dataset and inefficient multimoda ...

Institute of Electrical Engineering2025

Traffic perception from aerial images using butterfly fields

Alexandre Massoud Alahi, Sven Kreiss, George Adaimi

Drones are nowadays considered as a valuable solution to monitor urban traffic. Object detectors face numerous challenges when dealing with high-resolution aerial images captured by drones, due to variations in altitude, viewing angle, and weather conditio ...

Pergamon-Elsevier Science Ltd2023

A computer vision system for deep learning-based detection of patient mobilization activities in the ICU

Alexandre Massoud Alahi

Early and frequent patient mobilization substantially mitigates risk for post-intensive care syndrome and long-term functional impairment. We developed and tested computer vision algorithms to detect patient mobilization activities occurring in an adult IC ...

Springer2019

Cascade of Descriptors to Detect and Track Objects Across Any Network of Cameras

Michel Bierlaire, Pierre Vandergheynst, Alexandre Massoud Alahi, Murat Kunt

Most of the multi-camera systems assume a well structured environment to detect and match objects across cameras. Cameras need to be fixed and calibrated. In this work, a novel system is presented to detect and match any objects in a network of uncalibrate ...

Elsevier2010

Vision-Based Scene Understanding with Sparsity Promoting Priors

Alexandre Massoud Alahi

Human beings are interested in understanding their environments and the dynamic content that fills their surroundings. For applications ranging from security to marketing, people have installed networks of cameras to capture the dynamic elements of scenes. ...

EPFL2011

DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation

Alexandre Massoud Alahi

Scalable Vector Graphics (SVG) are ubiquitous in modern 2D interfaces due to their ability to scale to different resolutions. However, despite the success of deep learning-based models applied to rasterized images, the problem of vector graphics representa ...

2020

Social-Pose: Enhancing Trajectory Prediction with Human Body Pose

Alexandre Massoud Alahi, Saeed Saadatnejad, Yang Gao

Accurate human trajectory prediction is one of the most crucial tasks for autonomous driving, ensuring its safety. Yet, existing models often fail to fully leverage the visual cues that humans subconsciously communicate when navigating the space. In this w ...

IEEE Institute of Electrical and Electronics Engineers2025

Enhancing discrete choice models with representation learning

Alexandre Massoud Alahi, Virginie Janine Camille Lurkin, Brian Alan Tappy-Sifringer

In discrete choice modeling (DCM), model misspecifications may lead to limited predictability and biased parameter estimates. In this paper, we propose a new approach for estimating choice models in which we divide the systematic part of the utility specif ...

2020

TIC-TAC: A Framework for Improved Covariance Estimation in Deep Heteroscedastic Regression

Mathieu Salzmann, Alexandre Massoud Alahi, Megh Hiren Shukla

Deep heteroscedastic regression involves jointly optimizing the mean and covariance of the predicted distribution using the negative log-likelihood. However, recent works show that this may result in sub-optimal convergence due to the challenges associated ...

2024

OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association

Alexandre Massoud Alahi, Sven Kreiss, Lorenzo Bertoni

Many image-based perception tasks can be formulated as detecting, associating and tracking semantic keypoints, e.g., human body pose estimation and tracking. In this work, we present a general framework that jointly detects and forms spatio-temporal keypoi ...

IEEE Institute of Electrical and Electronics Engineers2022

Beyond Bits: Reconstructing Images from Local Binary Descriptors

Pierre Vandergheynst, Alexandre Massoud Alahi, Emmanuel D'Angelo

Local Binary Descriptors (LBDs) are good at matching image parts, but how much information is actually carried? Surprisingly, this question is usually ignored and replaced by a comparison of matching performances. In this paper, we directly address it by t ...

2012

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

Automated Detection of Isolated REM Sleep Behavior Disorder Using Computer Vision

Alexandre Massoud Alahi, George Adaimi, Mohamed Ossama Ahmed Abdelfattah

Objective: Isolated rapid eye movement (REM) sleep behavior disorder (iRBD) is, in most cases, an early stage of Parkinson's disease or related disorders. Diagnosis requires an overnight video-polysomnogram (vPSG), however, even for sleep experts, interpre ...

2025

Collaborative Sampling in Generative Adversarial Networks

Alexandre Massoud Alahi, Yuejiang Liu, Parth Ashit Kothari

The standard practice in Generative Adversarial Networks (GANs) discards the discriminator during sampling. However, this sampling method loses valuable information learned by the discriminator regarding the data distribution. In this work, we propose a co ...

2020

Human Trajectory Forecasting in Crowds: A Deep Learning Perspective

Alexandre Massoud Alahi, Sven Kreiss, Parth Ashit Kothari

Since the past few decades, human trajectory forecasting has been a field of active research owing to its numerous real-world applications: evacuation situation analysis, traffic operations, deployment of social robots in crowded environments, to name a fe ...

IEEE Institute of Electrical and Electronics Engineers2021

VL4Pose: Active Learning Through Out-Of-Distribution Detection For Pose Estimation

Alexandre Massoud Alahi, Megh Hiren Shukla

Advances in computing have enabled widespread access to pose estimation, creating new sources of data streams. Unlike mock set-ups for data collection, tapping into these data streams through on-device active learning allows us to directly sample from the ...

BMVA Press2022