基于全局自适应有向图的行人轨迹预测

孔玮; 刘云; 李辉; 崔雪红; 杨浩冉

doi:10.12263/DZXB.20211613

您当前的位置：

首页 >

文章列表页 >

基于全局自适应有向图的行人轨迹预测

学术论文 | 更新时间：2025-12-08

- 基于全局自适应有向图的行人轨迹预测
- Pedestrian Trajectory Prediction Based on Global Adaptive Directed Graph
- 电子学报 2022年50卷第8期页码：1905-1916
- 作者机构：
  
  青岛科技大学信息科学技术学院，山东青岛 266061
- 作者简介：
  
  [ "孔玮女，1986年生，山东济南人.青岛科技大学信息科学技术学院博士研究生.主要研究方向为计算机视觉、轨迹预测." ]
  [ "刘云男，1962年生，山西太原人.青岛科技大学信息科学技术学院教授、博士生导师.主要研究方向为计算机视觉、轨迹预测等." ]
  [ "李辉男，1984年生，河南平顶山人.青岛科技大学信息科学技术学院副教授、硕士生导师.主要研究方向为计算机视觉、多目标跟踪与轨迹预测等." ]
  [ "崔雪红女，1978年生，山东菏泽人.青岛科技大学信息科学技术学院高级实验师.主要研究方向为计算机视觉、多目标检测与跟踪." ]
  [ "杨浩冉女，1997年生，河北邯郸人.青岛科技大学信息科学技术学院硕士研究生.主要研究方向为3D点云目标跟踪." ]
- 基金信息：
  
  国家自然科学基金(61702295);山东省高等学校优秀青年创新团队计划(2019KJN047)
- DOI：10.12263/DZXB.20211613
  中图分类号： TP391.4;
- 收稿：2021-12-03，
  
  修回：2022-02-07，
  
  纸质出版：2022-08-25
- 稿件说明：
移动端阅览
孔玮,刘云,李辉等.基于全局自适应有向图的行人轨迹预测[J].电子学报,2022,50(08):1905-1916.

KONG Wei,LIU Yun,LI Hui,et al.Pedestrian Trajectory Prediction Based on Global Adaptive Directed Graph[J].ACTA ELECTRONICA SINICA,2022,50(08):1905-1916.
孔玮,刘云,李辉等.基于全局自适应有向图的行人轨迹预测[J].电子学报,2022,50(08):1905-1916. DOI： 10.12263/DZXB.20211613.

KONG Wei,LIU Yun,LI Hui,et al.Pedestrian Trajectory Prediction Based on Global Adaptive Directed Graph[J].ACTA ELECTRONICA SINICA,2022,50(08):1905-1916. DOI： 10.12263/DZXB.20211613.

摘要

由于行人交互的复杂性和周围环境的多变性，行人轨迹预测仍是一项具有挑战性的任务.然而，基于图结构的方法建模行人之间的交互时，存在着网络感受野小、成对行人间的相互交互对称、固定的图结构不能适应场景变化的问题，导致预测轨迹与真实轨迹偏差较大.为了解决这些问题，本文提出一种基于全局自适应有向图的行人轨迹预测方法（pedestrian trajectory prediction method based on Global Adaptive Directed Graph，GADG）.设计全局特征更新（Global Feature Updating，GFU）和全局特征选择（Global Feature Selection，GFS）分别提升空间域和时间域的网络感受范围，以获取全局交互特征.构建有向特征图，定义行人间的不对称交互，提高网络建模的方向性.建立自适应图模型，灵活调整行人间的交互关系，减少冗余连接，增强图模型的自适应能力.在ETH和UCY数据集上的实验结果表明，与最优值相比，平均位移误差降低14%，最终位移误差降低3%.

Abstract

Due to the complexity of pedestrian interaction and the variability of the surrounding environment

pedestrian trajectory prediction is still a challenging task. However

when modeling pedestrian interaction based on graph structure

there are some problems

such as small sensing field of the network

symmetrical interaction between pedestrians

and fixed graph structure that can not adapt to scene changes

which lead to a large deviation of the predicted trajectory from the real trajectory. To solve these problems

a pedestrian trajectory prediction method based on global adaptive directed graph is proposed. Global feature updating(GFU) and global feature selection(GFS) are designed to improve the perception range in spatial and temporal domain respectively and get global interaction features. A directed feature graph is constructed to define the asymmetric interaction between pedestrians and improve the directionality of network modeling. An adaptive graph model is established to flexibly adjust the relationship between pedestrians

reduce redundant connections and enhance the adaptive ability of the graph. The experimental results on ETH and UCY datasets show that comparing with the optimal value

the average displacement error is reduced by 14% and the final displacement error is reduced by 3%.

关键词

Keywords

references

孔玮 , 刘云 , 李辉 , 等 . 基于深度学习的行人轨迹预测方法综述 [J]. 控制与决策 , 2021 , 36 ( 12 ): 2841 - 2850 .

KONG W , LIU Y , LI H , et al . Survey of pedestrian trajectory prediction methods based on deep learning [J]. Control and Decision , 2021 , 36 ( 12 ): 2841 - 2850 . (in Chinese)

WU P X , CHEN S H , METAXAS D N . MotionNet: Joint perception and motion prediction for autonomous driving based on bird's eye view maps [C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR) . Seattle : IEEE , 2020 : 11382 - 11392 .

LUO Y F , CAI P P , BERA A , et al . PORCA: Modeling and planning for autonomous driving among many pedestrians [J]. IEEE Robotics and Automation Letters , 2018 , 3 ( 4 ): 3418 - 3425 .

HU T , LONG C J , XIAO C X . A novel visual representation on text using diverse conditional GAN for visual recognition [J]. IEEE Transactions on Image Processing , 2021 , 30 : 3499 - 3512 .

SALEH F , ALIAKBARIAN S , SALZMANN M , et al . ArTIST: Autoregressive trajectory inpainting and scoring for tracking [EB/OL]. ( 2020-04-16 )[ 2021-12 ]. https://arxiv.org/abs/2004.07482 https://arxiv.org/abs/2004.07482 .

LIANG J W , JIANG L , NIEBLES J C , et al . Peeking into the future: Predicting future person activities and locations in videos [C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR) . Long Beach, CA, USA : IEEE , 2019 : 5718 - 5727 .

张顺 , 龚怡宏 , 王进军 . 深度卷积神经网络的发展及其在计算机视觉领域的应用 [J]. 计算机学报 , 2019 , 42 ( 3 ): 453 - 482 .

ZHANG S , GONG Y H , WANG J J . The development of deep convolution neural network and its applications on computer vision [J]. Chinese Journal of Computers , 2019 , 42 ( 3 ): 453 - 482 . (in Chinese)

ALAHI A , GOEL K , RAMANATHAN V , et al . Social LSTM: Human trajectory prediction in crowded spaces [C]// 2016 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR) . Las Vegas, NV, USA : IEEE , 2016 : 961 - 971 .

GUPTA A , JOHNSON J , LI F F , et al . Social GAN: Socially acceptable trajectories with generative adversarial networks [C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR) . Salt Lake City, UT, USA : IEEE , 2018 : 2255 - 2264 .

SUN H , ZHAO Z Q , HE Z H . Reciprocal learning networks for human trajectory prediction [C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR) . Seattle : IEEE , 2020 : 7414 - 7423 .

YANG B , YAN G C , WANG P , et al . TPPO: A novel trajectory predictor with pseudo oracle [EB/OL]. ( 2020-02-04 )[ 2021-12 ]. https://arxiv.org/abs/2002.01852 https://arxiv.org/abs/2002.01852 .

SADEGHIAN A , KOSARAJU V , SADEGHIAN A , et al . SoPhie: An attentive GAN for predicting paths compliant to social and physical constraints [C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR) . Long Beach, CA, USA : IEEE , 2019 : 1349 - 1358 .

IVANOVIC B , PAVONE M . The trajectron: probabilistic multi-agent trajectory modeling with dynamic spatiotemporal graphs [C]// 2019 IEEE/CVF International Conference on Computer Vision(ICCV) . Seoul, Korea : IEEE , 2019 : 2375 - 2384 .

HUANG Y F , BI H K , LI Z X , et al . STGAT: Modeling spatial-temporal interactions for human trajectory prediction [C]// 2019 IEEE/CVF International Conference on Computer Vision(ICCV) . Seoul, Korea : IEEE , 2019 : 6271 - 6280 .

MOHAMED A , QIAN K , ELHOSEINY M , et al . Social-STGCNN: A social spatio-temporal graph convolutional neural network for human trajectory prediction [C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR) . Seattle : IEEE , 2020 : 14412 - 14420 .

WANG C X , CAI S F , TAN G . GraphTCN: Spatio-temporal interaction modeling for human trajectory prediction [C]// 2021 IEEE Winter Conference on Applications of Computer Vision(WACV) . Waikoloa : IEEE , 2021 : 3449 - 3458 .

毛琳 , 巩欣飞 , 杨大伟 , 等 . 空时社交关系池化行人轨迹预测模型 [J]. 计算机辅助设计与图形学学报 , 2020 , 32 ( 12 ): 1918 - 1925 .

MAO L , GONG X F , YANG D W , et al . Space-time social relationship pooling pedestrian trajectory prediction model [J]. Journal of Computer-Aided Design & Computer Graphics , 2020 , 32 ( 12 ): 1918 - 1925 . (in Chinese)

KOSARAJU V , SADEGHIAN A , MARTÍN-MARTÍN R , et al . Social-BiGAT: Multimodal trajectory forecasting using bicycle-GAN and graph attention networks [C]// 33rd Annual Conference on Neural Information Processing Systems(NIPS) . Vancouver, BC, Canada : NIPS , 2019 : 1 - 10 .

HADDAD S , WU M Q , WEI H , et al . Situation-aware pedestrian trajectory prediction with spatio-temporal attention model [EB/OL]. ( 2019-02-13 )[ 2021-12 ]. https://arxiv.org/abs/1902.05437 https://arxiv.org/abs/1902.05437 .

YU C J , MA X , REN J W , et al . Spatio-Temporal graph transformer networks for pedestrian trajectory Prediction [C]// European Conference on Computer Vision(ECCV) . Glasgow, UK : Springer , 2020 : 507 - 523 .

LIANG J W , JIANG L , MURPHY K , et al . The garden of forking paths: Towards multi-future trajectory prediction [C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR) . Seattle : IEEE , 2020 : 10505 - 10515 .

丰艳 , 张甜甜 , 王传旭 . 基于伪3D残差网络与交互关系建模的群组行为识别方法 [J]. 电子学报 , 2020 , 48 ( 7 ): 1269 - 1275 .

FENG Y , ZHANG T T , WANG C X . Group activity recognition method based on pseudo 3D residual network and interaction modeling [J]. Acta Electronica Sinica , 2020 , 48 ( 7 ): 1269 - 1275 . (in Chinese)

ZHAO T Y , XU Y F , MONFORT M , et al . Multi-agent tensor fusion for contextual trajectory prediction [C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR) . Long Beach, CA, USA : IEEE , 2019 : 12118 - 12126 .

程媛 , 迟荣华 , 黄少滨 , 等 . 基于非参数密度估计的不确定轨迹预测方法 [J]. 自动化学报 , 2019 , 45 ( 4 ): 787 - 798 .

CHENG Y , CHI R H , HUANG S B , et al . Uncertain trajectory prediction method using non-parametric density estimation [J]. Acta Automatica Sinica , 2019 , 45 ( 4 ): 787 - 798 . (in Chinese)

ZHANG L D , SHE Q , GUO P . Stochastic trajectory prediction with social graph network [EB/OL]. ( 2019-07-24 )[ 2021-12 ]. https://arxiv.org/abs/1907.10233 https://arxiv.org/abs/1907.10233 .

SUN J H , JIANG Q H , LU C W . Recursive social behavior graph for trajectory prediction [C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR) . Seattle : IEEE , 2020 : 657 - 666 .

SU H , DONG Y P , ZHU J , et al . Crowd scene understanding with coherent recurrent neural networks [C]// Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI) . New York, NY, USA : IJCAI , 2016 : 3469 - 3476 .

VEMULA A , MUELLING K , OH J . Social attention: Modeling attention in human crowds [C]// 2018 IEEE International Conference on Robotics and Automation(ICRA) . Brisbane, QLD, Australia : IEEE , 2018 : 4601 - 4607 .

LIU Q , WU S , WANG L , et al . Predicting the next location: A recurrent model with spatial and temporal contexts [C]// 30th AAAI Conference on Artificial Intelligence(AAAI) . Phoenix, AZ, USA : AAAI , 2016 : 194 - 200 .

ZHANG P , OUYANG W L , ZHANG P F , et al . SR-LSTM: State refinement for LSTM towards pedestrian trajectory prediction [C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR) . Long Beach, CA, USA : IEEE , 2019 : 12077 - 12086 .

YI S , LI H S , WANG X G . Pedestrian behavior understanding and prediction with deep neural networks [C]// 14th European Conference on Computer Vision(ECCV) . Amsterdam, Netherlands : Springer , 2016 : 263 - 279 .

YAGI T , MANGALAM K , YONETANI R , et al . Future person localization in first-person videos [C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR) . Salt Lake City, UT, USA : IEEE , 2018 : 7593 - 7602 .

LI J C , MA H B , TOMIZUKA M . Conditional generative neural system for probabilistic trajectory prediction [C]// 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS) . Macau, China : IEEE , 2019 : 6150 - 6156 .

SHAFIEE N , PADIR T , ELHAMIFAR E . Introvert: Human trajectory prediction via conditional 3D attention [C]// 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR) . Nashville : IEEE , 2021 : 16810 - 16820 .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于多维动态拓扑学习图卷积的骨架动作识别

基于自适应连续时间的群智感知轨迹隐私保护方案

基于多流空间注意力图卷积SRU网络的骨架动作识别

基于卡尔曼滤波的折反射全向图像目标跟踪