Abstract
The density of vehicles on roads has increased manifold over the last few decades. A seamless system to detect and track a particular vehicle can solve many problems, like traffic congestion, etc. This paper proposes the use of real-time data taken from closed-circuit televisions to detect and track the movements of vehicles. The feature extraction and classification are completely done by the process of faster RCNN (regional convolutional neural networks). The core work is based on region proposal networks. CNN furthers the generation of features; classification is done separately, but it is aided by RCNN, which includes deep learning as well as training the neural network. This is used along with the Kanade Lukas Tomasi algorithm to tack the desired features extracted. This method is simulated on MATLAB software.
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