Application of Recurrent Convolution Neural Network for Vehicle Detection

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.