JRDB-Act: A Large-scale Dataset for Spatio-temporal Action, Social Group and Activity Detection

TL;DR

JRDB-Act is a comprehensive large-scale dataset capturing diverse human actions and social interactions in real-world campus environments, enabling advanced research in action and social group detection.

Contribution

It introduces JRDB-Act, a large-scale, densely annotated dataset with over 2.8 million action labels and social group annotations, addressing the lack of real-world, long-tailed action datasets.

Findings

Developed an end-to-end pipeline for action and social group detection.

Provided extensive annotations including pose-based and interaction-based labels.

Made dataset and evaluation code publicly available.

Abstract

The availability of large-scale video action understanding datasets has facilitated advances in the interpretation of visual scenes containing people. 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. In this paper, we introduce JRDB-Act, as an extension of the existing JRDB, which is captured by a social mobile manipulator and reflects a real distribution of human daily-life actions in a university campus environment. JRDB-Act has been densely annotated with atomic actions, comprises over 2.8M action labels, constituting a large-scale spatio-temporal action detection dataset. Each human bounding box is labeled with one pose-based action label and multiple~(optional) interaction-based action labels. Moreover JRDB-Act provides social group annotation, conducive to the task of grouping individuals based on their interactions in the scene to infer their social activities~(common activities in each social group). Each annotated label in JRDB-Act is tagged with the annotators' confidence level which contributes to the development of reliable evaluation strategies. In order to demonstrate how one can effectively utilise such annotations, we develop an end-to-end trainable pipeline to learn and infer these tasks, i.e. individual action and social group detection. The data and the evaluation code is publicly available at https://jrdb.erc.monash.edu/.