Recursive Social Behavior Graph for Trajectory Prediction
- URL: http://arxiv.org/abs/2004.10402v1
- Date: Wed, 22 Apr 2020 06:01:48 GMT
- Title: Recursive Social Behavior Graph for Trajectory Prediction
- Authors: Jianhua Sun, Qinhong Jiang, Cewu Lu
- Abstract summary: We formulate social representations supervised by group-based annotations into a social behavior graph, called Recursive Social Behavior Graph.
With the guidance of Recursive Social Behavior Graph, we surpass state-of-the-art method on ETH and UCY dataset for 11.1% in ADE and 10.8% in FDE.
- Score: 49.005219590582676
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Social interaction is an important topic in human trajectory prediction to
generate plausible paths. In this paper, we present a novel insight of
group-based social interaction model to explore relationships among
pedestrians. We recursively extract social representations supervised by
group-based annotations and formulate them into a social behavior graph, called
Recursive Social Behavior Graph. Our recursive mechanism explores the
representation power largely. Graph Convolutional Neural Network then is used
to propagate social interaction information in such a graph. With the guidance
of Recursive Social Behavior Graph, we surpass state-of-the-art method on ETH
and UCY dataset for 11.1% in ADE and 10.8% in FDE in average, and successfully
predict complex social behaviors.
Related papers
- Geometric Graph Neural Network Modeling of Human Interactions in Crowded Environments [3.7752830020595787]
This paper proposes a geometric graph neural network architecture that integrates domain knowledge from psychological studies to model pedestrian interactions and predict future trajectories.
Evaluations across multiple datasets demonstrate improved prediction accuracy through reduced average and final displacement error metrics.
arXiv Detail & Related papers (2024-10-22T20:33:10Z) - From a Social Cognitive Perspective: Context-aware Visual Social Relationship Recognition [59.57095498284501]
We propose a novel approach that recognizes textbfContextual textbfSocial textbfRelationships (textbfConSoR) from a social cognitive perspective.
We construct social-aware descriptive language prompts with social relationships for each image.
Impressively, ConSoR outperforms previous methods with a 12.2% gain on the People-in-Social-Context (PISC) dataset and a 9.8% increase on the People-in-Photo-Album (PIPA) benchmark.
arXiv Detail & Related papers (2024-06-12T16:02:28Z) - Graph Neural Networks for Antisocial Behavior Detection on Twitter [0.0]
Social media resurgence of antisocial behavior has exerted a downward spiral on stereotypical beliefs, and hateful comments towards individuals and social groups.
Advances in graph neural networks employed on massive quantities of graph-structured data raise high hopes for the future of mediating communication on social media platforms.
An approach based on graph convolutional data was employed to better capture the dependencies between the heterogeneous types of data.
arXiv Detail & Related papers (2023-12-28T00:25:12Z) - SSAGCN: Social Soft Attention Graph Convolution Network for Pedestrian
Trajectory Prediction [59.064925464991056]
We propose one new prediction model named Social Soft Attention Graph Convolution Network (SSAGCN)
SSAGCN aims to simultaneously handle social interactions among pedestrians and scene interactions between pedestrians and environments.
Experiments on public available datasets prove the effectiveness of SSAGCN and have achieved state-of-the-art results.
arXiv Detail & Related papers (2021-12-05T01:49:18Z) - Social Processes: Self-Supervised Forecasting of Nonverbal Cues in
Social Conversations [22.302509912465077]
We take the first step in the direction of a bottom-up self-supervised approach in the domain of social human interactions.
We formulate the task of Social Cue Forecasting to leverage the larger amount of unlabeled low-level behavior cues.
We propose the Social Process (SP) models--socially aware sequence-to-sequence (Seq2Seq) models within the Neural Process (NP) family.
arXiv Detail & Related papers (2021-07-28T18:01:08Z) - SocAoG: Incremental Graph Parsing for Social Relation Inference in
Dialogues [112.94918467195637]
Inferring social relations from dialogues is vital for building emotionally intelligent robots.
We model the social network as an And-or Graph, named SocAoG, for the consistency of relations among a group.
Empirical results on DialogRE and MovieGraph show that our model infers social relations more accurately than the state-of-the-art methods.
arXiv Detail & Related papers (2021-06-02T08:07:42Z) - PHASE: PHysically-grounded Abstract Social Events for Machine Social
Perception [50.551003004553806]
We create a dataset of physically-grounded abstract social events, PHASE, that resemble a wide range of real-life social interactions.
Phase is validated with human experiments demonstrating that humans perceive rich interactions in the social events.
As a baseline model, we introduce a Bayesian inverse planning approach, SIMPLE, which outperforms state-of-the-art feed-forward neural networks.
arXiv Detail & Related papers (2021-03-02T18:44:57Z) - Graph-Based Social Relation Reasoning [101.9402771161935]
We propose a graph relational reasoning network (GR2N) for social relation recognition.
Our method considers the paradigm of jointly inferring the relations by constructing a social relation graph.
Experimental results illustrate that our method generates a reasonable and consistent social relation graph.
arXiv Detail & Related papers (2020-07-15T03:01:11Z) - Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural
Network for Human Trajectory Prediction [26.28051910420762]
Social Spatio-Temporal Graph Convolutional Neural Network (Social-STGCNN) modeled pedestrian interactions as a graph.
Our results show an improvement over the state of art by 20% on the Final Displacement Error (FDE) and an improvement on the Average Displacement Error (ADE) with up to 48 times faster inference speed.
We propose a kernel function to embed the social interactions between pedestrians within the adjacency matrix.
arXiv Detail & Related papers (2020-02-27T05:40:23Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.