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A Comparative Analysis of Cloud Based Watson System and CNN for Gesture Recognition Systems

机译：基于云的Watson系统和CNN用于手势识别系统的比较分析

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According to census 2011, the number of disabled people in India is 2.68 crores. Out of those, about 19 percent have a problem in hearing. With the advent of Convolutional Neural Networks (CNN) deployed on the host system and multi cloud platforms like IBM Watson, an important challenge faced by the developers is selection of suitable architecture for deployment. The ability of CNN to model non-linear relationships enables it to be used widely in biomedical domain and thus for the problem of disabled people. Recognition models deployed on Cloud offer on-demand secure storage, analysis and rapid scalability of services. This paper aims at providing a comparative study between the two architectures. For the first type of architecture the gesture of a mute person is recognized using image processing and CNN. Whereas second architecture uses cloud based visual recognizer to recognize the gestures. The prominent parameters such as recognition accuracy, angled detection and response time that play an important role when deploying the two architectures are measured and provide a perspective over the selection of architecture. The accuracy obtained for the CNN model is 98% and 97% for the cloud-based Watson model for the trained tested classes.
机译：根据2011年的人口普查，印度的残疾人人数为268千万。其中约有19％的人听力有问题。随着将卷积神经网络（CNN）部署在主机系统和IBM Watson等多云平台上，开发人员面临的一个重要挑战是选择合适的部署架构。 CNN建模非线性关系的能力使其能够在生物医学领域得到广泛应用，从而解决了残疾人的问题。部署在云上的识别模型提供按需安全存储，分析和服务的快速可伸缩性。本文旨在提供两种体系结构之间的比较研究。对于第一种类型的体系结构，使用图像处理和CNN识别静音人的手势。而第二体系结构使用基于云的视觉识别器来识别手势。在部署这两种架构时，诸如识别精度，角度检测和响应时间等重要参数起着重要作用，这些参数被测量，并为选择架构提供了一个视角。对于经过训练的测验类，CNN模型的基于CNN模型的准确性为98％，基于云的Watson模型的准确性为97％。

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