一种新的人体动作识别方法:改进LBG算法的离散HMM

Zhongxin Qu, Tingshu Lu, XiaoJiong Liu, Qianqian Wu, Mingjiang Wang
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引用次数: 3

摘要

隐马尔可夫模型(HMM)算法和矢量量化(VQ)算法在语音识别领域得到了广泛的应用。本文的创新之处在于将以上两种算法引入到人体动作识别中,并将其作为连续多帧视频动作识别的解决方案。模拟退火算法和空腔处理算法对矢量量化算法进行了改进,得到了全局最优码本。新算法的识别效果明显优于原有算法和传统算法。该方法实现了异常行为的识别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A new method for human action recognition: Discrete HMM with improved LBG algorithm
Hidden Markov Model (HMM) algorithm and Vector Quantization (VQ) algorithm are widely used in the field of speech recognition. The innovation of this paper will be the introduction of the above two algorithms into human action recognition and making them as a solution to recognize action of the continuous multi frames video. Simulated Annealing algorithm and the empty cavity processing algorithm improve vector quantization algorithm and obtain the global optimal codebook. The recognition result of the new algorithm is much better than the original algorithm and traditional algorithms. The new method realizes the identification of abnormal behavior.
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