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Found 6 papers, 1 papers with code
Social-MAE: Social Masked Autoencoder for Multi-person Motion Representation Learning
The framework uses masked modeling to pre-train the encoder to reconstruct masked human joint trajectories, enabling it to learn generalizable and data efficient representations of motion in human crowded scenes.
JRDB-Act: A Large-scale Dataset for Spatio-temporal Action, Social Group and Activity Detection
However, learning to recognise human actions and their social interactions in an unconstrained real-world environment comprising numerous people, with potentially highly unbalanced and long-tailed distributed action labels from a stream of sensory data captured from a mobile robot platform remains a significant challenge, not least owing to the lack of a reflective large-scale dataset.
TRiPOD: Human Trajectory and Pose Dynamics Forecasting in the Wild
Joint forecasting of human trajectory and pose dynamics is a fundamental building block of various applications ranging from robotics and autonomous driving to surveillance systems.
Looking Beyond Two Frames: End-to-End Multi-Object Tracking Using Spatial and Temporal Transformers
Tracking a time-varying indefinite number of objects in a video sequence over time remains a challenge despite recent advances in the field.
Attend And Discriminate: Beyond the State-of-the-Art for Human Activity Recognition using Wearable Sensors
Wearables are fundamental to improving our understanding of human activities, especially for an increasing number of healthcare applications from rehabilitation to fine-grained gait analysis.
Joint Learning of Social Groups, Individuals Action and Sub-group Activities in Videos
In this paper, we solve the problem of simultaneously grouping people by their social interactions, predicting their individual actions and the social activity of each social group, which we call the social task.
