Improving Social Awareness Through DANTE: A Deep Affinity Network for Clustering Conversational Interactants

Abstract

We propose a data-driven approach to detect conversational groups by identifying spatial arrangements typical of these focused social encounters. Our approach uses a novel Deep Affinity Network (DANTE) to predict the likelihood that two individuals in a scene are part of the same conversational group, considering their social context. The predicted pair-wise affinities are then used in a graph clustering framework to identify both small (e.g., dyads) and large groups. The results from our evaluation on multiple, established benchmarks suggest that combining powerful deep learning methods with classical clustering techniques can improve the detection of conversational groups in comparison to prior approaches. Finally, we demonstrate the practicality of our approach in a human-robot interaction scenario. Our efforts show that our work advances group detection not only in theory, but also in practice.

References in corpus (5)

• Adam: A Method for Stochastic Optimization
• OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
• F-formation Detection: Individuating Free-standing Conversational Groups in Images
• Deep Local Trajectory Replanning and Control for Robot Navigation
• Conversational Group Detection With Deep Convolutional Networks

Cited by in corpus (3)

• JRDB: A Dataset and Benchmark of Egocentric Robot Visual Perception of Humans in Built Environments
• Detecting socially interacting groups using f-formation: A survey of taxonomy, methods, datasets, applications, challenges, and future research directions
• Learning a Group-Aware Policy for Robot Navigation