Publications by Weizhe Liu | EPFL Graph Search

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Human-centered scene understanding is the process of perceiving and analysing a dynamic scene observed through a network of sensors with emphasis on human-related activities. It includes the visual perception of human-related activities from either single ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

Weizhe Liu

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Human-Centered Scene Understanding via Crowd Counting

Counting People by Estimating People Flows

Learning to Align Sequential Actions in the Wild

Context-Aware Crowd Counting

Multi-view Tracking UsingWeakly Supervised Human Motion Prediction

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

Leveraging Self-Supervision for Cross-Domain Crowd Counting

Estimating People Flows to Better Count Them in Crowded Scenes

Multi-view Tracking Using Weakly Supervised Human Motion Prediction