机器人人机交互中的干扰动作排除方法研究

Bulletin of Science and Technology（2015）

Cited 0|Views10

Abstract

有效的动作识别是实现机器人人机交互的关键,在进行人机交互过程中,采用图像识别的方法进行人体动作特征提取和感知,实现动作重构和识别,由于人体动作的随机性,导致大量的干扰动作,需要设计有效的干扰动作排除方法.提出一种基于局部线性嵌入人体动作重构的机器人人机交互中的干扰动作排除方法.建立特征空间背景全局性信息模型,对特征空间中的突变信息进行采集,采用图像处理方法实现对人体动作的特征模型构建,筛选出干扰动作特征,预测人体的动作点,构建全局非显著性二维流形集合矩阵,实现对干扰动作的排除,利用颜色梯度变化对像素点进行局部修正处理,进行干扰动作排除,可以提高对人体动作识别的抗干扰能力.仿真实验结果表明,采用该算法进行人机交互干扰动作排除和识别,能有效提高机器人人机交互的动作识别准确率,抗干扰能力强.

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Key words

robot,interpersonal interaction,action recognition,image

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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【要点】：论文提出了基于局部线性嵌入的人体动作重构方法，以消除人际交互中的干扰动作，创新点在于提高了动作识别的准确性和抗干扰能力。

【方法】：研究采用了图像处理方法提取人体动作特征，并利用全局信息模型构建特征背景，通过局部线性嵌入重构动作。

【实验】：实验通过模拟人机交互场景，采用所提出算法对干扰动作进行识别和消除，使用数据集为模拟交互场景中拍摄的动作图像。结果显示，该方法能有效提高机器人动作识别的准确性，并具有强大的抗干扰能力。