Pedestrian Trajectory Prediction Method Using Dynamic Scene Information Based Transformer Generative Adversarial Network

PEI Zhao; QIU Wen-tao; WANG Miao; MA Miao; ZHANG Yan-ning

doi:10.12263/DZXB.20210762

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Pedestrian Trajectory Prediction Method Using Dynamic Scene Information Based Transformer Generative Adversarial Network

PAPERS | 更新时间：2025-12-08

- Pedestrian Trajectory Prediction Method Using Dynamic Scene Information Based Transformer Generative Adversarial Network
- ACTA ELECTRONICA SINICA Vol. 50, Issue 7, Pages: 1537-1547(2022)
- 作者机构：
  
  1.陕西师范大学现代教学技术教育部重点实验室,陕西西安 710119
  2.陕西师范大学计算机科学学院,陕西西安 710119
  3.上海交通大学航空航天学院, 上海 200240
  4.空天地海一体化大数据应用技术国家工程实验室,陕西西安 710129
  5.西北工业大学计算机学院, 陕西西安 710129
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20210762
  CLC： TP391
- Received：16 June 2021，
  
  Revised：2021-11-07，
  
  Published：25 July 2022
- 稿件说明：
移动端阅览
裴炤,邱文涛,王淼等.基于Transformer动态场景信息生成对抗网络的行人轨迹预测方法[J].电子学报,2022,50(07):1537-1547.

PEI Zhao,QIU Wen-tao,WANG Miao,et al.Pedestrian Trajectory Prediction Method Using Dynamic Scene Information Based Transformer Generative Adversarial Network[J].ACTA ELECTRONICA SINICA,2022,50(07):1537-1547.
裴炤,邱文涛,王淼等.基于Transformer动态场景信息生成对抗网络的行人轨迹预测方法[J].电子学报,2022,50(07):1537-1547. DOI： 10.12263/DZXB.20210762.

PEI Zhao,QIU Wen-tao,WANG Miao,et al.Pedestrian Trajectory Prediction Method Using Dynamic Scene Information Based Transformer Generative Adversarial Network[J].ACTA ELECTRONICA SINICA,2022,50(07):1537-1547. DOI： 10.12263/DZXB.20210762.

摘要

行人轨迹预测是视频监控的重要组成部分，因现有方法未充分利用场景特征信息造成其预测轨迹不符合生活常识，导致行人轨迹预测精度较低出现明显偏离真实轨迹的情况.针对上述不足本文提出一种基于Transformer动态场景信息生成对抗网络（Generative Adversarial Network，GAN）的行人轨迹预测方法.该方法利用动态场景特征提取模块的卷积神经网络（Convolutional Neural Networks，CNN）模型对目标行人的动态场景信息进行特征提取，同时生成器网络中的编码器利用Transformer对行人的社会交互信息特征以及轨迹信息特征进行建模.在ETH和UCY数据集上的实验结果表明，与Social GAN模型相比，本文方法在多个场景下的平均位移误差准确率提高了25.61%，最终位移误差准确率提高了38.44%.

Abstract

Pedestrian trajectory prediction is an important part of video surveillance. The current methods are not accurate and sometimes violate common senses because scene information is not fully used. To eliminate the above shortcomings

this paper proposes a transformer generated adversarial network(GAN) algorithm which combines dynamic scene information with pedestrian social interaction information. The convolution neural network model of the dynamic scene extraction module is utilized to extract the dynamic scene information features of the target pedestrian

and the encoder in the generator network uses transformer to model the features of social interaction information and trajectory information of pedestrians. Experimental results on ETH and UCY datasets show that

compared with social GAN model

our method improves the accuracy of average displacement error by 25.61% and the accuracy of average final displacement error by 38.44% in multiple scenarios.

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references

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