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- 2018
一种集成卷积神经网络和深信网的步态识别与模拟方法
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Abstract:
摘要： 针对高斯过程的条件受限玻尔兹曼机(Gaussian-based conditional restricted Boltzmann machine, GCRBM)时序模型可以对单一种类的步态时序数据进行很好的预测,但对多类步态时序数据难以识别和预测的问题,提出一种集成卷积神经网络(convolutional neural network, CNN)和深信网(deep belief network, DBN)的步态识别与模拟方法。利用所有类步态数据训练多个不同结构的CNNs模型,利用多类数据训练多个DBNs模型学习低维特征,并通过低维特征训练多个GCRBMs模型。在步态识别与模拟时,CNNs分类器通过投票法确定步态数据的类别;通过识别到的类所对应的DBNs模型低维特征作为对应GCRBMs模型的输入预测目标数据的后期时序低维特征;利用DBNs重构阶段将后期时序低维特征模拟出步态图像。在CASIA系列步态数据集上的试验结果表明:与支持向量机(support vector machine, SVM)、集成DBN和CNN等方法相比,本研究方法的识别率有一定的提高,提出的模型能够根据步态时序预测结果模拟出真实的步态序列图像,证实了模型的有效性。
Abstract: The Gaussian-based conditional restricted Boltzmann machine(GCRBM)time series model could efficiently predict for single type of gait time series data, but the model could not make accurate recognition and prediction for multi-category gait time series data. To solve the problem above, an ensemble/integrated method with convolutional neural network(CNN)and deep belief network(DBN)for gait recognition and simulation was proposed. Multiple CNNs models with different structures were trained by all the gait data. Multiple DBNs models corresponding to the multi-category data were trained to study low dimensional features, and corresponding to train multiple GCRBMs models through the low dimensional features. In the step of recognition and simulation, model will identify the class of gait data with all CNNs classifiers by the “minority-obeying” voting strategy, then the low-dimensional feature of the DBNs model corresponding to the identified class was used as the input of the corresponding GCRBMs model to predict the late timing low-dimensional feature of the target data. The gait images could be reconstructed by the corresponding DBNs model. Compared with the method of support vector machine(SVM), integrated DBN and CNN, the proposed method’s gait recognition rate was improved based on CASIA gait datasets. Moreover, the predicting result could be simulated to the true gait sequences by the proposed method, which demonstrated the validity of the model
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