基于二阶隐马尔可夫模型的连续手语识别

在传统的一阶隐马尔可夫模型(HMM1)中, 状态序列中的每一个状态被假设只与前一个状态有关, 这样虽然可以简单、有效地推导出模型的学习和识别算法, 但也丢失了许多从上文传递下来的信息. 因此, 在传统一阶隐马尔可夫模型的基础上, 为了解决手语识别困难、正确率低的问题, 提出了一种基于二阶隐马尔可夫模型(HMM2)的连续手语识别方法. 该方法利用滑动窗口算法使手语视频切分成多个手语短视频, 通过三维卷积模型得到手语短视频和手语词汇视频的特征向量, 由此计算出二阶隐马尔可夫模型的相关参数, 并运用Viterbi算法实现连续手语的识别. 实验证明, 基于二阶隐马尔可夫模型的手语识别取得了88.6%的识别准确率, 高于传统的一阶隐马尔可夫模型.

Continuous Sign Language Recognition Based on Second-order Hidden Markov Model

In the traditional first-order hidden Markov model (HMM1), each state in the state sequence is assumed to be only related to the previous state. In this way, although the model learning and recognition algorithm can be simply and effectively deduced, a lot of information passed down from the above is lost. Therefore, in view of the traditional HMM1, a continuous sign language recognition method based on the second-order hidden Markov model (HMM2) is proposed to solve the problems of the difficulty and low accuracy of sign language recognition. In this method, a video of sign language is divided into several short videos by the sliding window algorithm, and the feature vectors of the short videos and word videos of sign language are obtained through the 3D convolution model. The relevant parameters of the HMM2 are thereby calculated, and continuous sign language recognition is achieved via the Viterbi algorithm. Experimental results show that the accuracy of sign language recognition based on the HMM2 is 88.6%, which is higher than that of the traditional HMM1.