通信学报 ›› 2016, Vol. 37 ›› Issue (5): 143-151.doi: 10.11959/j.issn.1000-436x.2016102

• 自动化技术、计算机技术 • 上一篇    下一篇

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   

  1. 南开大学计算机与控制工程学院,天津300350
  • 出版日期:2016-05-25 发布日期:2016-06-01
  • 基金资助:
    高等学校博士学科点专项科研基金资助项目;天津市重大科技专项基金资助项目

Identifying the confidence level of activity recognition via HMM

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

  1. College of Computer and Control Engineering,Nankai U iversity,Tianjin 300350,China
  • Online:2016-05-25 Published:2016-06-01
  • Supported by:
    Research Fund for the Doctoral Program of Higher Education of China;The Key Project in Tianjin Science & Technology Pillar Program

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。

关键词: 动作识别, 隐马尔可夫模型, 混淆率, 可信度

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.

Key words: activity recognition, hidden Markov model, confusion rate, confidence level

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