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Multi-view Tracking Using Weakly Supervised Human Motion Prediction

Pascal Fua, Martin Pierre Engilberge, Weizhe Liu

Multi-view approaches to people-tracking have the potential to better handle occlusions than single-view ones in crowded scenes. They often rely on the tracking-by-detection paradigm, which involves detecting people first and then connecting the detections ...

2023

Multi-view Tracking UsingWeakly Supervised Human Motion Prediction

Pascal Fua, Martin Pierre Engilberge, Weizhe Liu

IEEE COMPUTER SOC2023

Learning to Align Sequential Actions in the Wild

Pascal Fua, Bugra Tekin, Weizhe Liu

State-of-the-art methods for self-supervised sequential action alignment rely on deep networks that find correspon- dences across videos in time. They either learn frame-to- frame mapping across sequences, which does not leverage temporal information, or a ...

IEEE2022

Leveraging Self-Supervision for Cross-Domain Crowd Counting

Pascal Fua, Nikita Durasov, Weizhe Liu

State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density. While effective, these data-driven approaches rely on large amount of data annotation to achieve good performance, which stops these models from ...

IEEE COMPUTER SOC2022

Counting People by Estimating People Flows

Pascal Fua, Mathieu Salzmann, Weizhe Liu

Modern methods for counting people in crowded scenes rely on deep networks to estimate people densities in individual images. As such, only very few take advantage of temporal consistency in video sequences, and those that do only impose weak smoothness co ...

2021

Estimating People Flows to Better Count Them in Crowded Scenes

Pascal Fua, Mathieu Salzmann, Weizhe Liu

2020

Geometric and Physical Constraints for Drone-Based Head Plane Crowd Density Estimation

Pascal Fua, Mathieu Salzmann, Weizhe Liu, Krzysztof Maciej Lis

State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density in the image plane. While useful for this purpose, this image- plane density has no immediate physical meaning because it is subject to perspecti ...

IEEE2019

Context-Aware Crowd Counting

Pascal Fua, Mathieu Salzmann, Weizhe Liu

State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density. They typically use the same filters over the whole image or over large image patches. Only then do they estimate local scale to compensate for p ...

IEEE/CVF2019

Semiautomatic Annotation of MOOC Forum Posts

Pierre Dillenbourg, Lukasz Kidzinski, Weizhe Liu

Massive online open courses' (MOOCs') students who use discussion forums have higher chances of finishing the course. However, little research has been conducted for understanding the underlying factors. One of the reasons which hinders the analysis is the ...

Springer-Verlag Singapore Pte Ltd2016

Weizhe Liu

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Multi-view Tracking Using Weakly Supervised Human Motion Prediction

Multi-view Tracking UsingWeakly Supervised Human Motion Prediction

Learning to Align Sequential Actions in the Wild

Leveraging Self-Supervision for Cross-Domain Crowd Counting

Human-Centered Scene Understanding via Crowd Counting

Counting People by Estimating People Flows

Estimating People Flows to Better Count Them in Crowded Scenes

Geometric and Physical Constraints for Drone-Based Head Plane Crowd Density Estimation

Context-Aware Crowd Counting

Semiautomatic Annotation of MOOC Forum Posts

Counting People by Estimating People Flows

Semiautomatic Annotation of MOOC Forum Posts

Geometric and Physical Constraints for Drone-Based Head Plane Crowd Density Estimation

Human-Centered Scene Understanding via Crowd Counting

Multi-view Tracking Using Weakly Supervised Human Motion Prediction

Leveraging Self-Supervision for Cross-Domain Crowd Counting

Multi-view Tracking UsingWeakly Supervised Human Motion Prediction

Learning to Align Sequential Actions in the Wild

Estimating People Flows to Better Count Them in Crowded Scenes

Context-Aware Crowd Counting