Scene Gated Social Graph: Pedestrian Trajectory Prediction Based on Dynamic Social Graphs and Scene Constraints

• URL: http://arxiv.org/abs/2010.05507v1
• Date: Mon, 12 Oct 2020 08:04:05 GMT
• Title: Scene Gated Social Graph: Pedestrian Trajectory Prediction Based on Dynamic Social Graphs and Scene Constraints
• Authors: Hao Xue, Du Q.Huynh, Mark Reynolds
• Abstract summary: We propose a novel trajectory prediction method named Scene Gated Social Graph (SGSG) In the proposed SGSG, dynamic graphs are used to describe the social relationship among pedestrians. We compare our SGSG against twenty state-of-the-art pedestrian trajectory prediction methods and the results show that the proposed method achieves superior performance.
• Score: 17.042179951736262
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pedestrian trajectory prediction is valuable for understanding human motion behaviors and it is challenging because of the social influence from other pedestrians, the scene constraints and the multimodal possibilities of predicted trajectories. Most existing methods only focus on two of the above three key elements. In order to jointly consider all these elements, we propose a novel trajectory prediction method named Scene Gated Social Graph (SGSG). In the proposed SGSG, dynamic graphs are used to describe the social relationship among pedestrians. The social and scene influences are taken into account through the scene gated social graph features which combine the encoded social graph features and semantic scene features. In addition, a VAE module is incorporated to learn the scene gated social feature and sample latent variables for generating multiple trajectories that are socially and environmentally acceptable. We compare our SGSG against twenty state-of-the-art pedestrian trajectory prediction methods and the results show that the proposed method achieves superior performance on two widely used trajectory prediction benchmarks.

Related papers

• Social-Transmotion: Promptable Human Trajectory Prediction [65.80068316170613]
  Social-Transmotion is a generic Transformer-based model that exploits diverse and numerous visual cues to predict human behavior. Our approach is validated on multiple datasets, including JTA, JRDB, Pedestrians and Cyclists in Road Traffic, and ETH-UCY.
  arXiv  Detail & Related papers  (2023-12-26T18:56:49Z)
• Conditioned Human Trajectory Prediction using Iterative Attention Blocks [70.36888514074022]
  We present a simple yet effective pedestrian trajectory prediction model aimed at pedestrians positions prediction in urban-like environments. Our model is a neural-based architecture that can run several layers of attention blocks and transformers in an iterative sequential fashion. We show that without explicit introduction of social masks, dynamical models, social pooling layers, or complicated graph-like structures, it is possible to produce on par results with SoTA models.
  arXiv  Detail & Related papers  (2022-06-29T07:49:48Z)
• 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)
• SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction [64.16212996247943]
  We present a Sparse Graph Convolution Network(SGCN) for pedestrian trajectory prediction. Specifically, the SGCN explicitly models the sparse directed interaction with a sparse directed spatial graph to capture adaptive interaction pedestrians. visualizations indicate that our method can capture adaptive interactions between pedestrians and their effective motion tendencies.
  arXiv  Detail & Related papers  (2021-04-04T03:17:42Z)
• Graph-SIM: A Graph-based Spatiotemporal Interaction Modelling for Pedestrian Action Prediction [10.580548257913843]
  We propose a novel graph-based model for predicting pedestrian crossing action. We introduce a new dataset that provides 3D bounding box and pedestrian behavioural annotations for the existing nuScenes dataset. Our approach achieves state-of-the-art performance by improving on various metrics by more than 15% in comparison to existing methods.
  arXiv  Detail & Related papers  (2020-12-03T18:28:27Z)
• Graph2Kernel Grid-LSTM: A Multi-Cued Model for Pedestrian Trajectory Prediction by Learning Adaptive Neighborhoods [10.57164270098353]
  We present a new perspective to interaction modeling by proposing that pedestrian neighborhoods can become adaptive in design. Our model outperforms state-of-the-art approaches that collate resembling features over several publicly-tested surveillance videos.
  arXiv  Detail & Related papers  (2020-07-03T19:05:48Z)
• CoMoGCN: Coherent Motion Aware Trajectory Prediction with Graph Representation [12.580809204729583]
  We propose a novel framework, coherent motion aware graph convolutional network (CoMoGCN), for trajectory prediction in crowded scenes with group constraints. Our method achieves state-of-the-art performance on several different trajectory prediction benchmarks, and the best average performance among all benchmarks considered.
  arXiv  Detail & Related papers  (2020-05-02T09:10:30Z)
• Recursive Social Behavior Graph for Trajectory Prediction [49.005219590582676]
  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.
  arXiv  Detail & Related papers  (2020-04-22T06:01:48Z)
• Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction [57.56466850377598]
  Reasoning over visual data is a desirable capability for robotics and vision-based applications. In this paper, we present a framework on graph to uncover relationships in different objects in the scene for reasoning about pedestrian intent. Pedestrian intent, defined as the future action of crossing or not-crossing the street, is a very crucial piece of information for autonomous vehicles.
  arXiv  Detail & Related papers  (2020-02-20T18:50:44Z)

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.