TY - GEN
T1 - Machine Learning Driven Multiple Human Activity Recognition and Tracking Using Radar Technology: A Review
AU - Yousaf, Jawad
AU - Maharana, Ishita
AU - Galdanci, Hameeda Mahmoud
AU - Yaghi, Maha
AU - Gad, Abdalla
AU - Hassan, Taimur
AU - Ghazal, Mohammed
PY - 2025/10/21
Y1 - 2025/10/21
N2 - In recent years, human detection and tracking have become crucial in military, search and rescue, and law enforcement agencies. Using machine learning techniques such as two-dimensional convolutional neural network (CNN), support vector machine (SVM), and ResNet in correct detection proves to increase efficiency with a great magnitude. This paper reviews the vast efficient methodologies used in data extraction and signal pre-processing for the effective detection and tracking of multiple human activities. The systematic reviews and quantitative analysis of existing research are thoroughly reviewed. Furthermore, it discusses the assessment and evaluation criteria based on the results extracted to showcase the challenges and opportunities of the methodologies discussed. Moreover, this study also presents the effective techniques for multiple human detection, tracking within a room and through the wall scenarios.
AB - In recent years, human detection and tracking have become crucial in military, search and rescue, and law enforcement agencies. Using machine learning techniques such as two-dimensional convolutional neural network (CNN), support vector machine (SVM), and ResNet in correct detection proves to increase efficiency with a great magnitude. This paper reviews the vast efficient methodologies used in data extraction and signal pre-processing for the effective detection and tracking of multiple human activities. The systematic reviews and quantitative analysis of existing research are thoroughly reviewed. Furthermore, it discusses the assessment and evaluation criteria based on the results extracted to showcase the challenges and opportunities of the methodologies discussed. Moreover, this study also presents the effective techniques for multiple human detection, tracking within a room and through the wall scenarios.
KW - CNN
KW - Detection
KW - Human Activity Recognition (HCA)
KW - Multiple Human
KW - Tracking
KW - Ultrawideband radars
UR - https://ieeexplore.ieee.org/document/11205230
UR - https://www.scopus.com/pages/publications/105021959377
U2 - 10.1109/FiCloud66139.2025.00064
DO - 10.1109/FiCloud66139.2025.00064
M3 - Conference publication
AN - SCOPUS:105021959377
T3 - Proceedings - International Conference on Future Internet of Things and Cloud
SP - 418
EP - 423
BT - 2025 12th International Conference on Future Internet of Things and Cloud (FiCloud)
PB - IEEE
T2 - 12th International Conference on Future Internet of Things and Cloud, FiCloud 2025
Y2 - 11 August 2025 through 13 August 2025
ER -