A Low-Cost Real-Time Embedded Vehicle Counting and Classification System for Traffic Management Applications

This paper explores the feasibility of using a low-cost embedded ARM-based system for real-time vehicle detection, classification and counting through image processing algorithms with the aim of knowing information about vehicular traffic in different roads and highways to improve the management of mobility and the functioning of cities. This paper proposes the implementation of a low cost system to identify and classify vehicles using an Embedded ARM based platform (ODROID XU4) with Ubuntu operating system. The algorithms used are based on the Open-source library (Intel OpenCV) and implemented in Python programming language. The experimentation carried out proved that the efficiency of the algorithm implemented was 95.35 %, but it can be improved by increasing the training sample.