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Application of Recurrent Convolution Neural Network for Vehicle Detection
Published in Institute of Electrical and Electronics Engineers Inc.
Pages: 120 - 123
Object tracking and monitoring is used for detection purpose. This research is found since last two decades. However, the scope is there for further improvement. Cur-rent deep neural network model is found suitable in this area exclusively. We examine the problem of vehicle discov-ery by the deep learning method. In addition, we have shown that the use of deep features extracted from a pre-formed network produces a more efficient and ac-curate means of monitoring. Fast Recurrent CNN, the use of these systems becomes bottleneck process with relevance to CNN's operation. Fast R-CNN solves this downside by applying the projected mechanism to use the region and then CNN. An RPN is a fully convex network that predicts object boundaries and object scores at each location simultaneous-ly. © 2019 IEEE.
About the journal
JournalData powered by Typeset2019 International Conference on Applied Machine Learning (ICAML)
PublisherData powered by TypesetInstitute of Electrical and Electronics Engineers Inc.
Open AccessNo