Alexandre Massoud Alahi

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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 from a single image. The ...

A system is presented to detect and match any objects with mobile cameras collaborating with fixed cameras observing the same scene. No training data is needed. Various object descriptors are studied based on grids of region descriptors. Region descriptors ...

In this work, we study neural network architectures that will reduce the number of infractions made by autonomous-driving agents. These agents control vehicles by providing future waypoints directly from a forward-facing camera. Building on top of the teac ...

Predicting human trajectories is essential for the safe operation of autonomous vehicles, yet current data-driven models often lack robustness in case of noisy inputs such as adversarial examples or imperfect observations. Although some trajectory predicti ...

Self-driving cars and delivery robots are set to shape the future of transportation, but they still have to learn how to co-exist with humans in close proximity. Autonomous systems need to detect pedestrians and understand the meaning of their actions befo ...

Scene graph generation (SGG) methods extract relationships between objects. While most methods focus on improving top-down approaches, which build a scene graph based on detected objects from an off-the-shelf object detector, there is a limited amount of w ...

Human ability to foresee the near future plays a key role in everyone's life to prevent potentially dangerous situations. To be able to make predictions is crucial when people have to interact with the surrounding environment. Modeling such capability can ...

Omnidirectional depth perception is essential for mobile robotics applications that require scene understanding across a full 360° field of view. Camera-based setups offer a cost-effective option by using stereo depth estimation to generate dense, high-res ...

Pedestrian image generation in the desired pose can be used in a wide range of applications e.g., person re-identification and tracking which are among the fundamental challenges in self-driving cars. This is a hard task because it should be invariant to a ...

Estimating 3D human poses from 2D images is challenging due to occlusions and projective acquisition. Learning-based approaches have been largely studied to address this challenge, both in single and multi-view setups. These solutions however fail to gener ...

Image synthesis in the desired semantic can be used in many tasks of self-driving cars giving us the possibility to enhance existing challenging datasets by realistic-looking images which we do not have enough. Our goal is to improve the image quality gene ...

Hand hygiene is essential for preventing hospital-acquired infections but is difficult to accurately track. The gold-standard (human auditors) is insufficient for assessing true overall compliance. Computer vision technology has the ability to perform more ...

Despite considerable progress in stereo depth estimation, omnidirectional imaging remains underexplored, mainly due to the lack of appropriate data. We introduce Helvipad, a real-world dataset for omnidirectional stereo depth estimation, consisting of 40K ...

Human trajectory forecasting in crowds, at its core, is a sequence prediction problem with specific challenges of capturing inter-sequence dependencies (social interactions) and consequently predicting socially-compliant multimodal distributions. In recent ...

Human trajectory forecasting in crowds presents the challenges of modelling social interactions and outputting collision-free multimodal distribution. Following the success of Social Generative Adversarial Networks (SGAN), recent works propose various GAN- ...

Predicting future trajectories is critical in autonomous driving, especially in preventing accidents involving humans, where a predictive agent’s ability to anticipate in advance is of utmost importance. Trajectory forecasting models, employed in fields su ...

Alexandre Massoud Alahi

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Detecting 32 Pedestrian Attributes for Autonomous Vehicles

Object Detection and Matching with Mobile Cameras Collaborating with Fixed Cameras

Training Traffic Light Behavior with End-to-End Learning

Learning online combinatorial stochastic policies with deep reinforcement

Certified Human Trajectory Prediction

Simple Yet Effective Action Recognition for Autonomous Driving

Composite Relationship Fields with Transformers for Scene Graph Generation

Long-term path prediction in urban scenarios using circular distributions

Enhancing Discrete Choice Models with Neural Networks

Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model

Pedestrian Image Generation for Self-driving Cars

MPL: Lifting 3D Human Pose from Multi-view 2D Poses

Next Steps for Image Synthesis using Semantic Segmentation

Automatic detection of hand hygiene using computer vision technology

HELVIPAD: A Real-World Dataset for Omnidirectional Stereo Depth Estimation

Interpretable Social Anchors for Human Trajectory Forecasting in Crowds

Safety-Compliant Generative Adversarial Networks for Human Trajectory Forecasting

JRDB-Traj: A Dataset and Benchmark for Trajectory Forecasting in Crowds

Next Steps for Social Force with Big Data

Automated Detection Of Isolated Rem Sleep Behavior Disorder (Irbd) During Single Night In-Lab Video-Polysomnography (Psg) Using Computer Vision