Personnel emotion recognition model for Internet of vehicles security monitoring in community public space

Abstract

In recent years, the Internet of vehicles (IOV) with intelligent networked automobiles as terminal node has gradually become the development trend of automotive industry and research hot spot in related fields. This is due to its characteristics of intelligence, networking, low-carbon and energy saving. Real time emotion recognition for drivers and pedestrians in the community can be utilized to prevent fatigue driving and malicious collision, keep safety verification and pedestrian safety detection. This paper mainly studies the face emotion recognition model that can be utilized for IOV. Considering the fluctuation of image acquisition perspective and image quality in the application scene of IOV, the natural scene video similar to vehicle environment and its galvanic skin response (GSR) are utilized to make the testing set of emotion recognition. Then an expression recognition model combining codec and Support Vector Machine classifier is proposed. Finally, emotion recognition testing is completed on the basis of Algorithm 1. The matching accuracy between the emotion recognition model and GSR is 82.01%. In the process of model testing, 189 effective videos are involved and 155 are correctly identified.

Signal processing of Internet of Vehicles based on intelligent interference

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With the development of artificial intelligence technology, in the future 6G era, various network models of deep learning will be ubiquitous. One of the most critical scenarios is that service providers need to interact and make decisions on vehicle information in real time [1,2].IoV is based on the Internet of Things [3,4] technology and is more oriented to transportation applications. The Internet of Things (IoT) has a wide range of applications in the current 5G era, especially in improving the data transmission rate.

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“…With the development of artificial intelligence technology, in the future 6G era, various network models of deep learning will be ubiquitous. One of the most critical scenarios is that service providers need to interact and make decisions on vehicle information in real time [1,2].…”