D-STOR: A Novel Framework of Deep-Semantic Traffic Object Recognition

Abstract

Deep learning techniques such as Convolutional Neural Networks (CNNs) have proven the efficiency in recognizing image objects. Moreover, this recognition work has been extended to discover relations among detected objects. Although this research line of mining semantic information in image has become more attractive, it was not investigated thoroughly. This paper introduces a deep-semantic traffic object recognition based on a knowledge model to reveal relations among detected objects, named D-STOR. In order to confirm the efficiency of the D-STOR framework, an experiment on a dataset of traffic images in Vietnam was conducted and then yielded promising experimental results.