Publications by Sven Kreiss | EPFL Graph Search

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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 ...

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 ...

Perceiving humans in the context of Intelligent Transportation Systems (ITS) often relies on multiple cameras or expensive LiDAR sensors. In this work, we present a new cost-effective vision-based method that perceives humans' locations in 3D and their bod ...

Mobility in an effective and socially-compliant manner is an essential yet challenging task for robots operating in crowded spaces. Recent works have shown the power of deep reinforcement learning techniques to learn socially cooperative policies. However, ...

Monocular and stereo visions are cost-effective solutions for 3D human localization in the context of self-driving cars or social robots. However, they are usually developed independently and have their respective strengths and limitations. We propose a no ...

We present an end-to-end trainable Neural Network architecture for stereo imaging that jointly locates and estimates human body poses in 3D. Our method defines a 2D pose for each human in a stereo pair of images and uses a correlation layer with a composit ...

We propose a new bottom-up method for multi-person 2D human pose estimation that is particularly well suited for urban mobility such as self-driving cars and delivery robots. The new method, PifPaf, uses a Part Intensity Field (PIF) to localize body parts ...

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 ...

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 ...

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 ...

Sven Kreiss

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Traffic perception from aerial images using butterfly fields

Human Trajectory Forecasting in Crowds: A Deep Learning Perspective

Perceiving Humans: from Monocular 3D Localization to Social Distancing

Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning

MonStereo: When Monocular and Stereo Meet at the Tail of 3D Human Localization

Joint Human Pose Estimation and Stereo 3D Localization

PifPaf: Composite Fields for Human Pose Estimation

MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimation

Deep Visual Re-Identification with Confidence

Deep visual Re-identification with confidence

Next Steps for Social Force with Big Data

Automatic Differentiation in Social Force Models

Keypoint Communities