Image Processing and Deep Neural Image Classification Based Physical Feature Determiner for Traffic Stakeholders

Abstract

Nowadays, image processing and deep learning is used in industrial and non-industrial areas. Addition to this, smart cities are very popular trend for the researchers and r&d workers. In the smart city applications, researchers and r&d workers present solutions about traffic, health, security and energy problems in the cities. The smart city applications for the traffic are focused on proposing solutions about detecting traffic violations, congestions, park spot suggestion, public transportations etc. We propose a solution for detecting traffic stakeholders physical features based on image processing and deep neural classification. The mentioned traffic stakeholders are automobiles, buses, trucks, trailers, motorcycles and pedestrians. We detect contours from the traffic videos which appropriate size for these traffic stakeholders then we crop these contours from the video first. Then we use the deep image classifier model for classification with detected contours. Addition to this we calculate vehicles dimensional features based on the contour size and determine colors based on HSV features. We intend with this study providing physical features to the smart city workers and researchers for using these features in their applications which controlling violations, determining statistics and the other applications like mentioned. For this reason, we provide this solution with a web service application in the future.