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Related Experiment Video

Updated: May 30, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Weakly supervised learning of interactions between humans and objects.

Alessandro Prest1, Cordelia Schmid, Vittorio Ferrari

  • 1ETH Zurich, Sternwartstrasse 7, Zurich CH-8092, Switzerland. prest@vision.ee.ethz.ch

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 3, 2011
PubMed
Summary
This summary is machine-generated.

This study presents a new weakly supervised method for recognizing human actions by focusing on human-object interactions. The approach learns automatically from images, identifying humans, relevant objects, and their spatial relationships for action understanding.

Related Experiment Videos

Last Updated: May 30, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Area of Science:

  • Computer Vision
  • Machine Learning
  • Human-Computer Interaction

Background:

  • Human action recognition is crucial for understanding complex visual scenes.
  • Current methods often require extensive, detailed annotations.
  • Modeling human-object interactions offers a more nuanced approach to action recognition.

Purpose of the Study:

  • To develop a weakly supervised method for learning human actions as interactions between humans and objects.
  • To create a human-centric approach that localizes humans and identifies relevant objects and their spatial relations.
  • To automatically learn models from images annotated only with action labels.

Main Methods:

  • A human-centric approach is employed, starting with human localization.
  • A robust human detector is built by combining existing part detectors for varied visibility.
  • The model determines the action object and its spatial relation to the human.
  • A probabilistic model of human-object interaction is generated.

Main Results:

  • The approach successfully learns human-object interaction models from weakly labeled data.
  • Experimental evaluations were conducted on multiple datasets, including sports actions and human-object interactions.
  • The method demonstrates effectiveness in recognizing actions based on human-object spatial relationships.

Conclusions:

  • Weakly supervised learning is effective for human action recognition, particularly when modeling human-object interactions.
  • The proposed human-centric method provides a robust way to learn action models automatically.
  • This research contributes to more sophisticated and less annotation-intensive action recognition systems.