The traffic surveillance system is accumulated with an enormous amount of data regarding road traffic each and every second. Monitoring these data with the human eye is a tedious process and it also requires manpower for monitoring. Deep learning approach (Convolutional Neural Network) can be utilized for traffic monitoring and control. The traffic surveillance data are pre-processed to construct the training dataset. The Traffic net is constructed by transferring the network to traffic applications and retraining it with self-established data set. This Traffic net can be used for regional detection in large scale applications.Further, it can be implemented across-the-board. The efficiency is admirably verified through speedy discovery in the high accuracy in the case study. The tentative assessment could pull out to its successful application to a traffic surveillance system and has potential enrichment for the intelligent transport system in future.
Road Traffic Condition Monitoring using Deep Learning
01.02.2020
2976415 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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