TY - GEN
T1 - Online Multi-Object Thermal Tracking and Reidentification using YOLO and DeepSORT in Low Light Environment
AU - Papudi, Ricky
AU - Fahrezi, Septian
AU - Sipahutar, Aldy
AU - Firjatullah, Bryan
AU - Ibrahim, Nur
AU - Kusumoputro, Benyamin
N1 - Publisher Copyright:
©2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Multi-object tracking and reidentification has been used in various domains such as surveillance, human-machine interface, and vehicle navigation. However, existing methods primarily rely on deep learning and tracking-by-detection (TBD) techniques using visible light cameras, which face challenges in low-light conditions. To address this, we propose a novel multi-object tracking approach utilizing thermal cameras to overcome visibility issues in such environments. Our method integrates the YOLOv7 for precise image detection and the DeepSORT algorithm for efficient tracking amidst occlusion and object similarities. Through rigorous experimentation, we achieved notable results, obtaining an IDF1 score of 0.911, F1 Score of 0.878, MOTA of 0.767, and MOTP of 0.161, underscoring the efficacy of our approach in multi-object tracking under challenging lighting conditions. Given the scarcity of publicly available thermal datasets, we curated a thermal reidentification dataset comprising 10,000 thermal images captured in diverse low-light settings. Our dataset includes various poses and perspectives to enhance model performance. We structure our study to present related works, detail our proposed methodology, and analyze experimental results, highlighting the efficacy of our approach in multi-object tracking under low-light conditions.
AB - Multi-object tracking and reidentification has been used in various domains such as surveillance, human-machine interface, and vehicle navigation. However, existing methods primarily rely on deep learning and tracking-by-detection (TBD) techniques using visible light cameras, which face challenges in low-light conditions. To address this, we propose a novel multi-object tracking approach utilizing thermal cameras to overcome visibility issues in such environments. Our method integrates the YOLOv7 for precise image detection and the DeepSORT algorithm for efficient tracking amidst occlusion and object similarities. Through rigorous experimentation, we achieved notable results, obtaining an IDF1 score of 0.911, F1 Score of 0.878, MOTA of 0.767, and MOTP of 0.161, underscoring the efficacy of our approach in multi-object tracking under challenging lighting conditions. Given the scarcity of publicly available thermal datasets, we curated a thermal reidentification dataset comprising 10,000 thermal images captured in diverse low-light settings. Our dataset includes various poses and perspectives to enhance model performance. We structure our study to present related works, detail our proposed methodology, and analyze experimental results, highlighting the efficacy of our approach in multi-object tracking under low-light conditions.
KW - data augmentation
KW - DeepSORT
KW - low-light environments
KW - Multi-object tracking
KW - thermal
KW - YOLOv7
UR - http://www.scopus.com/inward/record.url?scp=85202301931&partnerID=8YFLogxK
U2 - 10.1109/IAICT62357.2024.10617558
DO - 10.1109/IAICT62357.2024.10617558
M3 - Conference contribution
AN - SCOPUS:85202301931
T3 - Proceedings of the 2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2024
SP - 14
EP - 20
BT - Proceedings of the 2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2024
Y2 - 4 July 2024 through 6 July 2024
ER -