  The traffic video data has become a critical factor in confining the state of
traffic congestion due to the recent advancements in computer vision. This work
proposes a unique technique for traffic video classification using a
color-coding scheme before training the traffic data in a Deep convolutional
neural network. At first, the video data is transformed into an imagery data
set; then, the vehicle detection is performed using the You Only Look Once
algorithm. A color-coded scheme has been adopted to transform the imagery
dataset into a binary image dataset. These binary images are fed to a Deep
Convolutional Neural Network. Using the UCSD dataset, we have obtained a
classification accuracy of 98.2%.
