Abstract
The intelligent transportation system in big data environment is the development trend of future transportation system, which effectively integrates advanced information technology, data communication transmission technology, electronic sensor technology, control technology and computer technology and is applied to overall ground transportation management. Hence, it establishes a real-time, accurate, efficient and comprehensive transportation management system that functions in a wide range and all-round aspects. In order to meet the demands of the intelligent transportation big data processing, this paper puts forward a high performance computing architecture of large-scale transportation video data management based on cloud computing, designs a parallel computing model containing the distributed file system and distributed computing system to solve the problems such as flexible server increase or decrease, load balancing and flexible dynamic storage increase or decrease, computing power and great improvement of storage efficiency. On the basis of this technical architecture, the system adopts BP neural network-related algorithms to extract the static transportation signs in road videos, and uses interframe difference algorithm and Gaussian mixture model (GMM) fusion algorithm to extract the moving targets in road transportation videos. In this way, they are taken as important integral parts and data sources of key frames of intelligent video image recognition to improve the recognition ability of key frames and eventually utilize semantic recognition model based on CNN (Convolutional Neural Network) to complete the intelligent recognition of whole transportation videos. Through network pressure test, computing ability test, recognition ability test and other tests, it has been proved that the intelligent transportation video processing system based on big data environment is successful and the design scheme of this system has strong practical application value.
Highlights
According to the reports of World Health Organization (WHO) and World Bank (WB), the road traffic fatalities will be the third most important factor affecting human health and longevity after heart disease and depression by 2020
More than 3,000 people die in road traffic accidents every day, but it is undeniable that road traffic accidents can be predicted and prevented
TECHNICAL MODEL OF INTELLIGENT TRANSPORTATION VIDEO PROCESSING SYSTEM IN BIG DATA ENVIRONMENT In order to meet the demands of intelligent transportation big data processing, maintain the flexibility of cloud server increase or decrease, reduce network pressure and guarantee load balancing, this paper designs parallel computing model and puts forward the technical architecture of distributed file system and computing system
Summary
According to the reports of World Health Organization (WHO) and World Bank (WB), the road traffic fatalities will be the third most important factor affecting human health and longevity after heart disease and depression by 2020. In order to meet the demands of intelligent transportation big data processing, maintain the flexibility of cloud server increase or decrease, reduce network pressure and guarantee load balancing, this paper designs parallel computing model and puts forward the technical architecture of distributed file system and computing system. To guarantee the effect of behavior recognition, this system classifies the recognition of video images into static traffic sign recognition and moving target recognition by combining with the characteristics of transportation videos and on the basis of this technical architecture After recognition, they are processed by the recognition algorithm based on key frames recognition technology and semantic processing, and the intelligent transportation behavior recognition is completed . It is necessary to utilize big data-related technology to explore and develop the videos, realize data sharing, processing and integration and achieve the purpose of intelligent service. It has a good effect when applied to this system
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