基于HMM的动作识别结果可信度计算方法

王昌海; 张建忠; 徐敬东; 许昱玮

doi:10.11959/j.issn.1000-436x.2016102

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基于HMM的动作识别结果可信度计算方法

自动化技术、计算机技术 | 更新时间：2024-06-05

- 基于HMM的动作识别结果可信度计算方法
- Identifying the confidence level of activity recognition via HMM
- 通信学报 2016年37卷第5期页码：143-151
- 作者机构：
  
  南开大学计算机与控制工程学院，天津300350
- 作者简介：
  
  [ "王昌海（1987-），男，山东聊城人，南开大学博士生，主要研究方向为动作识别、移动情景感知、室内定位。" ]
  [ "张建忠（1964-），男，河北石家庄人，博士，南开大学教授、博士生导师，主要研究方向为移动计算、对等计算、网络安全。" ]
  [ "徐敬东（1965-），女，辽宁锦州人，博士，南开大学教授、博士生导师，主要研究方向为移动计算、信息安全、网络管理。" ]
  [ "许昱玮（1985-），男，安徽黄山人，博士，南开大学讲师、硕士生导师，主要研究方向为移动计算、无线网络、车载自组织网络。" ]
- 基金信息：
  
  高等学校博士学科点专项科研基金资助项目(20120031110035);天津市重大科技专项基金资助项目(13ZCZDGX01098)
- DOI：10.11959/j.issn.1000-436x.2016102
  中图分类号： TP399
- 网络出版日期：2016-05，
  
  纸质出版日期：2016-05-15
- 稿件说明：
移动端阅览
王昌海, 张建忠, 徐敬东, 等. 基于HMM的动作识别结果可信度计算方法[J]. 通信学报, 2016,37(5):143-151.

Chang-hai WANG, Jian-zhong ZHANG, Jing-dong XU, et al. Identifying the confidence level of activity recognition via HMM[J]. Journal on communications, 2016, 37(5): 143-151.
王昌海, 张建忠, 徐敬东, 等. 基于HMM的动作识别结果可信度计算方法[J]. 通信学报, 2016,37(5):143-151. DOI： 10.11959/j.issn.1000-436x.2016102.

Chang-hai WANG, Jian-zhong ZHANG, Jing-dong XU, et al. Identifying the confidence level of activity recognition via HMM[J]. Journal on communications, 2016, 37(5): 143-151. DOI： 10.11959/j.issn.1000-436x.2016102.

摘要

针对当前动作识别可信度计算方法中混淆率高、不适用于迁移学习等问题，提出一种基于样本上下文信息的可信度计算方法（S-HMM

sliding windows hidden Markov model）。该方法使用隐马尔可夫模型（HMM

hidden Markov model）理论对识别结果序列建模，将样本所在序列识别正确的概率作为识别结果的可信度，避免了当前可信度计算方法依赖于样本在特征空间中分布的问题。实验使用真实场景中的数据进行仿真，结果表明，与现有方法相比，该方法可将可信度混淆率降低37%左右。

Abstract

A context-based method to identify the confidence level of activ recognition was proposed

referred to as S-HMM(sliding window hidden Markov model)

which reduced the confusion rate and facilitated the transfer learning.With S-HMM

the activity recognition sequence was modeled as HMM(hidden Markov model)and the corresponding probability was adopted as the confidence level.This

S-HMM removed the dependency of the confidence level on the sample distribution in the feature space.S-HMM is extensively evaluated based on real-life activity data

demonstrat-ing a reduced confusion rate of 37% when compared to the state-of-the-art methods.

关键词

Keywords

references

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