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基于行人姿态的轨迹预测方法

王瑞平 宋晓 陈凯 龚开奇 张峻凡

王瑞平,宋晓,陈凯,等. 基于行人姿态的轨迹预测方法[J]. 北京航空航天大学学报,2023,49(7):1743-1754 doi: 10.13700/j.bh.1001-5965.2021.0557
引用本文: 王瑞平,宋晓,陈凯,等. 基于行人姿态的轨迹预测方法[J]. 北京航空航天大学学报,2023,49(7):1743-1754 doi: 10.13700/j.bh.1001-5965.2021.0557
WANG R P,SONG X,CHEN K,et al. Pedestrian trajectory prediction method based on pedestrian pose[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(7):1743-1754 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0557
Citation: WANG R P,SONG X,CHEN K,et al. Pedestrian trajectory prediction method based on pedestrian pose[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(7):1743-1754 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0557

基于行人姿态的轨迹预测方法

doi: 10.13700/j.bh.1001-5965.2021.0557
基金项目: 国家重点研发计划(2018YFB1702703)
详细信息
    通讯作者:

    E-mail:songxiao@buaa.edu.cn

  • 中图分类号: V221+.3;TB553

Pedestrian trajectory prediction method based on pedestrian pose

Funds: National Key R & D Program of China (2018YFB1702703)
More Information
  • 摘要:

    在自动驾驶领域,行人轨迹预测一直是研究热点之一,行人行为的不确定性给轨迹预测带来很大的挑战。目前大部分轨迹预测方法只专注于行人之间的信息交互,忽略了行人意图和场景中其他语义信息对行人轨迹的影响。为此,提出一种基于行人姿态的卷积编码器-解码器网络(PKCEDN)来预测目标行人轨迹的方法,所提方法包含基于卷积、长短时记忆(LSTM)网络的编码器-解码器模型和能够学习当前时刻与过去时刻轨迹相关性的注意力机制。所提方法在MOT16、MOT17和MOT20公开数据集上进行了相关测试,与Linear、LSTM、Social-LSTM、Social-生成对抗网络(GAN)、SR-LSTM和Msgtv等主流方法相比,在保证预测速度不降低的前提下,平均误差降低约36%。

     

  • 图 1  姿态表征行人意图

    Figure 1.  Pose representation of pedestrian intention

    图 2  PKCEDN的结构

    Figure 2.  Structure of PKCEDN

    图 3  Conv-LSTM网络结构

    Figure 3.  Structure of Conv-LSTM network

    图 4  空间语义信息分类

    Figure 4.  Spatial semantic information classification

    图 5  行人姿态建模与行人意图

    Figure 5.  Pedestrian intention modeling with pedestrian pose

    图 6  编码器-解码器中的注意力模块详细结构

    Figure 6.  Detailed structure attention module for encoder-decoder architecture

    图 7  MOT16数据集上,预测轨迹定性结果的可视化

    Figure 7.  Trajectories predictions qualitative results visualization on MOT16 datasets

    图 8  MOT20数据集上,预测轨迹定性结果的可视化

    Figure 8.  Trajectories predictions qualitative results visualization on MOT20 datasets

    表  1  网络层模型参数列表

    Table  1.   Network layer model parameters list

    网络层输入输出
    Layer116@11×1132@11×11
    Layer232@11×1164@11×11
    Layer364@11×11128@11×11
    Deconv2@1×1128@11×11
    Conv-LSTM (Decoder Module)128@11×11128@11×11
    Conv128@11×112@1×1
    下载: 导出CSV

    表  2  本文方法与主流方法的轨迹预测结果在MOT16数据集上的比较

    Table  2.   Comparison of trajectory prediction results of proposed method and state-of-the-art methods on MOT16 dataset 像素

    方法 ADE ADE均值
    MOT16-02 MOT16-04 MOT16-09 MOT16-10 MOT16-13
    LSTM 30.12 32.15 30.52 30.96 31.73 31.096
    Social-LSTM 29.13 30.86 31.25 29.85 29.95 30.208
    Social-GAN 27.63 29.42 28.75 29.54 30.83 29.234
    SR-LSTM 28.73 29.65 30.74 29.31 29.03 29.492
    Msgtv 27.32 29.21 27.49 30.38 29.72 28.824
    本文方法 19.15 17.46 19.54 16.75 18.43 18.266
    方法 FDE FDE均值
    MOT16-02 MOT16-04 MOT16-09 MOT16-10 MOT16-13
    LSTM 44.64 43.85 41.64 42.43 40.45 42.602
    Social-LSTM 43.53 41.85 40.64 39.56 40.75 41.266
    Social-GAN 34.54 35.46 32.64 37.85 38.85 35.868
    SR-LSTM 38.35 37.31 38.62 34.18 32.48 36.188
    Msgtv 33.07 34.38 31.83 38.01 35.74 34.606
    本文方法21.11 20.43 23.66 20.78 22.54 21.704
    下载: 导出CSV

    表  3  本文方法与主流方法的轨迹预测结果在MOT17数据集上的比较

    Table  3.   Comparison of trajectory prediction results of PKCEDN and state-of-the-art methods on MOT17 dataset 像素

    方法 ADE ADE均值
    MOT17-02 MOT17-03 MOT17-08 MOT17-11 MOT17-14
    LSTM 35.15 36.31 33.85 35.21 39.38 35.98
    Social-LSTM 30.01 31.48 32.89 35.02 33.64 32.608
    Social-GAN 28.79 30.13 28.62 30.93 30.48 29.79
    SR-LSTM 29.13 30.53 29.24 30.15 29.61 29.732
    Msgtv 26.53 30.13 28.75 29.32 27.54 28.454
    本文方法 20.04 17.86 18.42 15.84 18.05 18.042
    方法 FDE FDE均值
    MOT17-02 MOT17-03 MOT17-08 MOT17-11 MOT17-14
    LSTM 50.64 55.97 61.57 60.02 63.42 58.324
    Social-LSTM 44.64 52.18 54.15 43.63 42.17 47.354
    Social-GAN 35.64 39.24 38.02 37.89 35.93 37.344
    SR-LSTM 36.73 40.13 37.85 36.47 34.64 37.164
    Msgtv 34.53 37.64 35.18 37.56 33.17 35.616
    本文方法24.75 23.84 25.03 22.41 25.75 24.356
    下载: 导出CSV

    表  4  本文方法与主流方法的轨迹预测结果在MOT20数据集上的比较

    Table  4.   Comparison of trajectory prediction results of PKCEDN and state-of-the-art methods on MOT20 dataset 像素

    方法 ADE ADE均值
    MOT20-01 MOT20-02 MOT20-03 MOT20-05
    LSTM 32.13 34.98 29.76 30.65 31.88
    Social-LSTM 30.10 33.21 30.41 29.98 30.93
    Social-GAN 27.02 26.98 26.81 24.06 26.22
    SR-LSTM 28.31 33.01 28.73 29.53 29.895
    Msgtv 28.64 27.16 26.14 24.98 26.730
    本文方法 18.64 17.85 20.13 14.43 17.763
    方法FDE FDE均值
    MOT20-01 MOT20-02 MOT20-03 MOT20-05
    LSTM 46.54 51.83 53.54 53.41 51.33
    Social-LSTM 43.83 44.12 43.84 40.75 43.14
    Social-GAN 32.79 30.81 33.56 31.84 32.25
    SR-LSTM 36.64 40.37 39.21 34.78 37.750
    Msgtv 33.69 32.42 34.15 31.19 32.863
    本文方法24.74 21.63 24.31 19..46 22.535
    下载: 导出CSV

    表  5  本文方法的消融实验

    Table  5.   Ablation study of proposed method

    算法参数数值(MOT16)/像素
    TenlMADE 24.455
    MFDE31.324
    Tenl + TenpMADE22.543
    MFDE30.041
    TenhMADE21.412
    MFDE27.544
    Tenp+TenhMADE18.266
    MFDE21.704
    下载: 导出CSV

    表  6  本文方法与主流方法在预处理和预测速度方面的比较

    Table  6.   Comparison with state-of-the-art methods on pre-processing speed and prediction speed

    方法 数据预处理模块 预测模块速度/(次·s−1) 速度提升/%
    LSTM 0.03 0.04 76.29
    Social-LSTM 2.13 3.21 1
    Social-GAN 0.20 0.26 11.60
    SR-LSTM 1.24 2.01 1.64
    Msgtv 0.24 0.38 8.61
    本文方法 0.54 0.28 6.51
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-09-26
  • 录用日期:  2022-01-02
  • 网络出版日期:  2022-02-24
  • 整期出版日期:  2023-07-31

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