Towards a low-cost embedded vehicle counting system based on deep-learning for traffic management applications

This paper explores the feasibility of using a low-cost embedded system for real-time vehicle detection and counting through the use of deep neural networks. It compares the performance of two different object tracking methods, the Kalman filter with the Hungarian algorithm and the centroid tracking algorithm. The experimentation proved that the efficiency of the implemented algorithms was above the 92% and 98% for the centroid tracking algorithm and Kalman filter with the Hungarian algorithm, respectively. Also, the Kalman filter produced fewer errors overcoming the centroid tracking algorithm.