Open AccessComparison of FairMOT-VGG16 and MCMOT Implementation for Multi-Object Tracking and Gender Detection on Mall CCTV
Author(s) -
Pray Somaldo,
Dina Chahyati
Publication year - 2021
Publication title -
jurnal ilmu komputer dan informasi (journal of computer science and information)/jurnal ilmu komputer dan informasi
Language(s) - English
Resource type - Journals
eISSN - 2502-9274
pISSN - 2088-7051
DOI - 10.21609/jiki.v14i1.958
Subject(s) - tracking (education) , computer science , shopping mall , artificial intelligence , computer vision , business , psychology , advertising , pedagogy
The crowd detection system on CCTV has proven to be useful for retail and shopping sector owners in mall areas. The data can be used as a guide by shopping center owners to find out the number of visitors who enter at a certain time. However, such information was still insufficient. The need for richer data has led to the development of more specific person detection which involves gender. Gender detection can provide specific information on the number of men and women visiting a particular location. However, gender detection alone does not provide an identity label for every detection that occurs, so it needs to be combined with a multi-person tracking system. This study compares two tracking methods with gender detection, namely FairMOT with gender classification and MCMOT. The first method produces MOTA, MOTP, IDS, and FPS of 78.56, 79.57, 19, and 24.4, while the second method produces 69.84, 81.94, 147, and 30.5. In addition, evaluation of gender was also carried out where the first method resulted in a gender accuracy of 65\% while the second method was 62.35\%.