Identifying the confidence level of activity recognition via HMM

Chang-hai WANG; Jian-zhong ZHANG; Jing-dong XU; Yu-wei XU

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

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Identifying the confidence level of activity recognition via HMM

Automation technology,computer technology | 更新时间：2024-06-05

- Identifying the confidence level of activity recognition via HMM
- Journal on Communications Vol. 37, Issue 5, Pages: 143-151(2016)
- 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：
- 基金信息：
  
  Specialized Research Fund for the Doctoral Program of Higher Education(20120031110035);The Key Project in Tianjin Science & Technology Pillar Program(13ZCZDGX01098)
- DOI：10.11959/j.issn.1000-436x.2016102
  CLC： TP399
- Online First：2016-05，
  
  Published：15 May 2016
- 稿件说明：
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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：

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.

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references

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