Predicting dominance in multi-person videos

Author:

Bai Chongyang1,Bolonkin Maksim1,Kumar Srijan2,Leskovec Jure2,Burgoon Judee3,Dunbar Norah4,Subrahmanian V. S.1

Affiliation:

1. Dartmouth College

2. Stanford University

3. University of Arizona

4. University of California Santa Barbara

Abstract

We consider the problems of predicting (i) the most dominant person in a group of people, and (ii) the more dominant of a pair of people, from videos depicting group interactions. We introduce a novel family of variables called Dominance Rank. We combine features not previously used for dominance prediction (e.g., facial action units, emotions), with a novel ensemble-based approach to solve these two problems. We test our models against four competing algorithms in the literature on two datasets and show that our results improve past performance. We show 2.4% to 16.7% improvement in AUC compared to baselines on one dataset, and a gain of 0.6% to 8.8% in accuracy on the other. Ablation testing shows that Dominance Rank features play a key role.

Publisher

International Joint Conferences on Artificial Intelligence Organization

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Co-Located Human–Human Interaction Analysis Using Nonverbal Cues: A Survey;ACM Computing Surveys;2023-11-25

2. DIPS: A Dyadic Impression Prediction System for Group Interaction Videos;ACM Transactions on Multimedia Computing, Communications, and Applications;2023-01-23

3. TEDIC: Neural Modeling of Behavioral Patterns in Dynamic Social Interaction Networks;Proceedings of the Web Conference 2021;2021-04-19

4. Iterative Collective Classification for Visual Focus of Attention Prediction;Terrorism, Security, and Computation;2021

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