结合有向场景运动模式的粒子滤波行人跟踪方法

屈鉴铭; 刘志镜; 贺文骅

doi:10.7652/xjtuxb201412012

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结合有向场景运动模式的粒子滤波行人跟踪方法

更新时间：2025-07-09

- 结合有向场景运动模式的粒子滤波行人跟踪方法
- Pedestrian Tracking Using Directed Scene Motion Pattern and Particle Filter
- 西安交通大学学报 2014年48卷第12期页码：74-79
- 作者机构：
  
  西安电子科技大学计算机学院,西安,710071
- 作者简介：
- 基金信息：
  
  : 2014-06-16。作者简介: 屈鉴铭(1985—),男,博士生;刘志镜(通信作者),男,教授,博士生导师。基金项目: 国家自然科学基金资助项目(61173091)
- DOI：10.7652/xjtuxb201412012
  中图分类号： TN911.73
- 网络首发：2014-12-10，
  
  纸质出版：2014
- 稿件说明：
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屈鉴铭, 刘志镜, 贺文骅. 结合有向场景运动模式的粒子滤波行人跟踪方法[J]. 西安交通大学学报, 2014,48(12):74-79.

Pedestrian Tracking Using Directed Scene Motion Pattern and Particle Filter[J]. 2014, 48(12): 74-79.
屈鉴铭, 刘志镜, 贺文骅. 结合有向场景运动模式的粒子滤波行人跟踪方法[J]. 西安交通大学学报, 2014,48(12):74-79. DOI： 10.7652/xjtuxb201412012.

Pedestrian Tracking Using Directed Scene Motion Pattern and Particle Filter[J]. 2014, 48(12): 74-79. DOI： 10.7652/xjtuxb201412012.

摘要

针对现有行人跟踪算法较少考虑场景运动模式信息的问题

建立一种面向有向场景运动模式的在线学习模型以描述区域行人的共有运动特性

并以此提出了一种新型的粒子滤波行人跟踪算法。通过对行人运动特性的选择性在线统计

探索在非高密度行人跟踪问题中场景模式信息和运动历史信息的运用方式。模型由一个表征行人运动状态相空间局域运动特性的二阶直方图矩阵来描述

并根据每个跟踪单元的加权投票实施更新。通过修正粒子转移后似然概率分布

该算法能够加速粒子向真实的后验分布收敛。通过对两个不同特点的公共数据集视频中的行人进行跟踪实验并与标准的粒子滤波算法结果比较

该算法的平均跟踪误差均低于标准粒子滤波平均跟踪误差的40%

且其运算速度可达6～15帧/s

满足近实时应用帧率。

Abstract

An online learning model oriented to directed scene motion patterns and based on particle filter is proposed to get over the problem that existing pedestrian tracking algorisms have little consideration on the scene motion patterns

to process the priori scene information and to improve the precision of pedestrian tracking. The model describes the local motion characteristic in phase space of the system state vector using a direction-speed motion histogram matrix

and is updated according to weighted voting of each track unit. The particle filter can accelerate the particles evolution to the true posterior distribution through modifying likelihood probability distribution of particle transfer. Experimental results on two pedestrian data sets with different characteristic forms and comparisons with the standard particle filter algorithm show that the average tracking error of the proposed approach is 40% lower than that of the standard particle filter algorithm

while its computation speed approaches the range of 6 to 15 frames a second

and that the proposed approach completely meets the conditions of real-time application.

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references

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