Talking Heads: Detecting Humans and Recognizing Their Interactions

@article{Hoai2014TalkingHD,
  title={Talking Heads: Detecting Humans and Recognizing Their Interactions},
  author={Minh Hoai and Andrew Zisserman},
  journal={2014 IEEE Conference on Computer Vision and Pattern Recognition},
  year={2014},
  pages={875-882}
}
  • Minh HoaiAndrew Zisserman
  • Published 23 June 2014
  • Computer Science
  • 2014 IEEE Conference on Computer Vision and Pattern Recognition
The objective of this work is to accurately and efficiently detect configurations of one or more people in edited TV material. [] Key Method We make the following contributions: first, we introduce a new learnable context aware configuration model for detecting sets of people in TV material that predicts the scale and location of each upper body in the configuration, second, we show that inference of the model can be solved globally and efficiently using dynamic programming, and implement a maximum margin…

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References

SHOWING 1-10 OF 31 REFERENCES

Detecting People Looking at Each Other in Videos

The objective of this work is to determine if people are interacting in TV video by detecting whether they are looking at each other or not. We determine both the temporal period of the interaction

Structured Learning of Human Interactions in TV Shows

It is shown that inference can be carried out with polynomial complexity in the number of people, and an efficient algorithm is described that is evaluated on a new dataset comprising 300 video clips acquired from 23 different TV shows and on the benchmark UT--Interaction dataset.

Recognizing proxemics in personal photos

This work presents a computational formulation of visual proxemics by attempting to label each pair of people in an image with a subset of physically based “touch codes” by building an articulated model tuned for each touch code.

Pictorial structures revisited: People detection and articulated pose estimation

This paper proposes a generic approach based on the pictorial structures framework, and demonstrates that such a model is equally suitable for both detection and pose estimation tasks, outperforming the state of the art on three recently proposed datasets.

Progressive search space reduction for human pose estimation

An approach that progressively reduces the search space for body parts, to greatly improve the chances that pose estimation will succeed, and an integrated spatio- temporal model covering multiple frames to refine pose estimates from individual frames, with inference using belief propagation.

Weakly Supervised Learning of Interactions between Humans and Objects

An extensive experimental evaluation on the sports action data set from [1], the PASCAL Action 2010 data set [2], and a new human-object interaction data set are presented.

Detection and Tracking of Occluded People

This work observes that typical occlusions are due to overlaps between people and proposes a people detector tailored to various occlusion levels, and leverages the fact that person/person Occlusion result in very characteristic appearance patterns that can help to improve detection results.

We Are Family: Joint Pose Estimation of Multiple Persons

A novel multi-person pose estimation framework, which extends pictorial structures (PS) to explicitly model interactions between people and to estimate their poses jointly, resulting in better pose estimates in group photos, where several persons stand nearby and occlude each other.

Cascaded Models for Articulated Pose Estimation

This work proposes to learn a sequence of structured models at different pose resolutions, where coarse models filter the pose space for the next level via their max-marginals, and trains the cascade to prune as much as possible while preserving true poses for the final level pictorial structure model.