Graph relation network for person counting in construction site using UAV

Abstract

Due to its portability and maneuverability, Unmanned Aerial Vehicles (UAVs) are increasingly used in industrial fields. Our work aims to develop a framework of person counting executed by an UAV, which can count the total number of persons accurately. To solve the problem of multiple counts for the same person, this work proposes a novel Graph Similarity-based Person Counting Network (GSPCN) which consists of three modules: person detection network, person re-identification module, and person counting module. To begin with, we detect the bounding boxes and the corresponding images for each object in image sequence. Secondly, we calculate the visual similarity between persons. And then, each person is taken as the root node to construct a graph, then we calculate the similarity between different graphs as the graph similarity. After that, we comprehensively consider the visual similarity and graph similarity to determine whether the person is repeated. Finally, by removing all duplicate persons, we can get the total number of persons. The proposed framework is tested in a real-world scenario and it empirically outperforms the existing state-of-the-art methods.