计算机科学 ›› 2020, Vol. 47 ›› Issue (6): 180-183.doi: 10.11896/jsjkx.200200030
景雨, 祁瑞华, 刘建鑫, 刘朝霞
JING Yu, QI Rui-hua, LIU Jian-xin, LIU Zhao-xia
摘要: 基于传统的浅层学习网络由于过度依赖于人工选择手势特征,因此不能实时适应复杂多变的自然场景。在卷积神经网络架构的基础上,提出了一种改进的多尺度深度网络手势识别模型,该模型能够利用卷积层自动学习手势特征,进而除去人工提取特征的弊端。该方法引入自适应多尺度特性来实现同一卷积层不同尺寸卷积核生成不同尺度特征,并通过级联浅层和深层的特征来达到不同抽象程度的特征图融合。同时,为了增强模型的泛化能力,提出了基于正则化约束的损失函数。实验结果表明,所提网络模型的识别精度高于普通单尺度卷积神经网络结构的识别精度,弥补了提取特征不够精细、全面及稳定性欠佳等缺点,同时网络训练所需的时间并没有大幅度增加。
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