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实验小鼠运动参数的模板匹配及粒子滤波提取方法

张继文 梁桐 张淑平

张继文, 梁桐, 张淑平. 实验小鼠运动参数的模板匹配及粒子滤波提取方法. 自动化学报, 2018, 44(1): 25-34. doi: 10.16383/j.aas.2018.c160573
引用本文: 张继文, 梁桐, 张淑平. 实验小鼠运动参数的模板匹配及粒子滤波提取方法. 自动化学报, 2018, 44(1): 25-34. doi: 10.16383/j.aas.2018.c160573
ZHANG Ji-Wen, LIANG Tong, ZHANG Shu-Ping. An Extraction Algorithm for Motion Parameters of A Laboratory Mouse by Model Matching and Particle Filtering. ACTA AUTOMATICA SINICA, 2018, 44(1): 25-34. doi: 10.16383/j.aas.2018.c160573
Citation: ZHANG Ji-Wen, LIANG Tong, ZHANG Shu-Ping. An Extraction Algorithm for Motion Parameters of A Laboratory Mouse by Model Matching and Particle Filtering. ACTA AUTOMATICA SINICA, 2018, 44(1): 25-34. doi: 10.16383/j.aas.2018.c160573

实验小鼠运动参数的模板匹配及粒子滤波提取方法

doi: 10.16383/j.aas.2018.c160573
基金项目: 

国家自然科学基金 61403225

摩擦学国家重点实验室 SKLT09A03

详细信息
    作者简介:

    张继文清华大学机械工程系助理研究员.2014年获得清华大学机械工程系机械工程博士学位.主要研究方向为仿人机器人, 运动规划, 环境感知与定位.E-mail:jwzhang@mail.tsinghua.edu.cn

    梁桐瑞士苏黎世大学脑研究所博士研究生.2013年获得清华大学生命科学学院硕士学位.主要研究方向为调节成体大脑神经干细胞活动的细胞和分子机理.E-mail:liangt10@126.com

    通讯作者:

    张淑平清华大学生命科学学院教授.1998年获得中国农业大学理学博士学位.主要研究方向为利用哺乳动物细胞和小鼠模型开展与发育及疾病相关的细胞信号转导机制的研究.本文通信作者.E-mail:bczhang@mail.tsinghua.edu.cn

An Extraction Algorithm for Motion Parameters of A Laboratory Mouse by Model Matching and Particle Filtering

Funds: 

National Natural Science Foundation of China 61403225

Project of State Key Laboratory of Tribology SKLT09A03

More Information
    Author Bio:

    Assistant researcher at the Department of Mechanical Engineering, Tsinghua University. He received his Ph. D. degree from Tsinghua University in 2014. His research interest covers humanoid robotics, motion planning, perception and localization

    Ph. D. candidate at the Brain Research Institute, University of Zurich. He received his master degree from the School of Life Sciences, Tsinghua University in 2013. His research interest covers cellular and molecular mechanisms that regulate stem cell activity in the developing and adult brain

    Corresponding author: ZHANG Shu-Ping Professor at the School of Life Sciences, Tsinghua University. She received her Ph. D. degree from China Agricultural University in 1998. Her research interest covers molecular mechanism of cell signaling using mammalian cells and mouse model. Corresponding author of this paper
  • 摘要: 实验小鼠是一种变形体对象,现有方法难以从连续视频图像中同时提取出运动轨迹和体态细节.本文采用模板匹配和粒子滤波的目标跟踪方法求解这一问题.提出了一种几何体部件模型,在引入小鼠移动速率的基础上给出了其运动状态方程,以二值化前景像素与几何部件模型间的差异度方程为观测模型,以状态方程及相互独立的多维随机变量为运动模型,从而建立起基本粒子滤波算法.与逐帧差分识别方法的对比实验研究表明,所提出的模型与实验小鼠形体相似,能够达到视频在线提取的计算效率.新方法在强噪声干扰条件下解决了运动轨迹和体态同时精确估计,并有效避免了首尾识别混淆及虚影干扰等困境,从而为后续生物学行为分析提供依据.
    1)  本文责任编委 吕金虎
  • 图  1  用于小鼠目标跟踪的模板匹配及粒子滤波方法

    Fig.  1  Model matching and particle filtering method applied to the object tracking of a laboratory mouse

    图  2  小鼠部件模型示意图

    Fig.  2  Illustration of the part model of a mouse

    图  3  小鼠以恒定的体态曲率 $\rho$ 及移动速率 $v$ 的匀速圆周运动

    Fig.  3  Uniform circular motion of a mouse with constant curvature $\rho $ and constant moving velocity $v$

    图  4  带有视频跟踪的小鼠行为学实验装置

    Fig.  4  Experimental device for behavior analysis of mice with video tracking

    图  5  不同光照条件下的视频拍摄截图

    Fig.  5  Snapshots of the video in different illuminating conditions

    图  6  背景减除及后续的图像预处理过程示例

    Fig.  6  An example of the background subtraction and the followed image preprocessing

    图  7  图像预处理与粒子滤波的小鼠运动参数提取流程图

    Fig.  7  Flow chart of the parameters for behavior analysis based on image preprocessing and particle filter

    图  8  蓝光照射条件下的一段小鼠目标跟踪结果

    Fig.  8  One piece of the object tracking result of a mouse in the blue illuminating condition

    图  9  模板匹配及粒子滤波方法在三种不同光照条件下所提取的实验小鼠整体运动轨迹提取结果

    Fig.  9  The extracting result of the moving trajectory of the laboratory mouse in three different illuminating conditions using model matching and particle filtering

    图  10  模板匹配及粒子滤波算法所提取的移动速度、体长变化及体态曲率结果

    Fig.  10  The extracting result of the motion speed, body length variation and body curvature using model matching and particle filtering

    图  11  单周期运动参数提取算法计算耗时直方图

    Fig.  11  Histogram of the computing time consumption of the motion parameter extraction algorithm for a single cycle

    图  12  采用帧间差分法在三种光照条件下所提取的目标小鼠整体运动轨迹提取结果

    Fig.  12  The extracting result of the moving trajectory of the object mouse in three different illuminating conditions using frame differencing method

    图  13  帧间差分法所提取的移动速度、体长变化及体态曲率结果

    Fig.  13  The extracting result of the motion speed, body length variation, and body curvature using frame differencing method

    表  1  基本粒子滤波算法列表[20]

    Table  1  Algorithm list of the basic particle filter [20]

    1: Function Particle_filter $\chi _t $ , $z_{t+1}$
    2:      $\bar {\chi }_{t+1} \leftarrow \emptyset,\chi _{t+1} \leftarrow \emptyset $
    3:      For $m = 1:M$
    4:         以 ${\rm {\pmb X}}_{t+1}^{[m]} \sim p({\rm {\pmb X}}_{t+1} \vert {\rm {\pmb X}}_t )$ 采样获得 ${\rm {\pmb X}}_{t+1}^{[m]} $
    5:         计算权重 $w_{t+1}^{[m]} \mbox{=}p(z_{t+1} \vert {\rm {\pmb X}}_{t+1}^{[m]} )$
    6:          $\bar {\chi }_{t+1} =\bar {\chi }_{t+1} +\left\langle {{\rm {\pmb X}}_{t+1}^{[m]} ,w_{t+1}^{[m]} } \right\rangle $
    7:      End for
    8:     依据 $\bar {\chi }_{t+1} $ 重采样生成 $\chi _{t+1} $
    9:      Return $\chi _{t+1} $
    10: End function
    下载: 导出CSV

    表  2  实验小鼠状态变量及模型差异度 $r$ 的正态分布参数

    Table  2  Parameters of the normalized distribution for the state variables and model-observation difference $r$ of a laboratory mouse

    变量 单位 均值 $\mu $ 标准差 $\sigma $
    位置 $x$ 像素 $0.00$ $3.00 $
    位置 $y$ 像素 $0.00$ $3.00 $
    姿态角 $\theta $ rad $0.00$ $0.09 $
    速度 $v$ 像素 $\cdot \text{s}^{-1}$ $0.00$ $25.00 $
    体态伸长 $e$ 像素 $0.00$ $3.00 $
    体态曲率 $\rho $ $\text{rad}\cdot$ 像素 $^{-1}$ $0.00$ $0.01 $
    模型差异度 $r$ 1 $0.00$ $1.00 $
    下载: 导出CSV
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出版历程
  • 收稿日期:  2016-08-04
  • 录用日期:  2017-02-03
  • 刊出日期:  2018-01-01

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