Device-free human presence detection using frequency domain

2015 ◽  
Author(s):  
Bojan Mrazovac ◽  
Branislav M. Todorovic ◽  
Dragan Kukolj ◽  
Miodrag Temerinac
Keyword(s):  
Frequency Domain ◽  
Human Presence ◽  
Human Presence Detection ◽  
Device Free

Device‐free indoor human presence detection method based on the information entropy of RSSI variations

2013 ◽  
Vol 49 (22) ◽  
pp. 1386-1388 ◽  
Author(s):  
B. Mrazovac ◽  
B.M. Todorović ◽  
M.Z. Bjelica ◽  
D. Kukolj
Keyword(s):  
Information Entropy ◽  
Detection Method ◽  
Human Presence ◽  
Human Presence Detection ◽  
Device Free

Human presence detection and tracking for a concierge robot

2004 ◽  
Vol 37 (8) ◽  
pp. 986-991
Author(s):  
Iñaki Rañó ◽  
Bogdan Raducanu ◽  
Sriram Subramanian
Keyword(s):  
Human Presence ◽  
Detection And Tracking ◽  
Human Presence Detection

Deep Learning Based Human Presence Detection

Mekatronika ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 55-61
Author(s):  
Venketaramana Balachandran ◽  
Muhammad Nur Aiman Shapiee ◽  
Ahmad Fakhri Ab. Nasir ◽  
Mohd Azraai Mohd Razman ◽  
Anwar P.P. Abdul Majeed
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Detection Task ◽  
Human Detection ◽  
Average Precision ◽  
Human Presence ◽  
The Public ◽  
Detection And Tracking ◽  
Human Presence Detection ◽  
Recent Variation

Human detection and tracking have been progressively demanded in various industries. The concern over human safety has inhibited the deployment of advanced and collaborative robotics, mainly attributed to the dimensionality limitation of present safety sensing. This study entails developing a deep learning-based human presence detector for deployment in smart factory environments to overcome dimensionality limitations. The objective is to develop a suitable human presence detector based on state-of-the-art YOLO variation to achieve real-time detection with high inference accuracy for feasible deployment at TT Vision Holdings Berhad. It will cover the fundamentals of modern deep learning based object detectors and the methods to accomplish the human presence detection task. The YOLO family of object detectors have truly revolutionized the Computer Vision and object detection industry and have continuously evolved since its development. At present, the most recent variation of YOLO includes YOLOv4 and YOLOv4 - Tiny. These models are acquired and pre-evaluated on the public CrowdHuman benchmark dataset. These algorithms mentioned are pre-trained on the CrowdHuman models and benchmarked at the preliminary stage. YOLOv4 and YOLOv4 – Tiny are trained on the CrowdHuman dataset for 4000 iterations and achieved a mean Average Precision of 78.21% at 25FPS and 55.59% 80FPS, respectively. The models are further fine-tuned on a  Custom CCTV dataset and achieved significant precision improvements up to 88.08% at 25 FPS and 77.70% at 80FPS, respectively. The final evaluation justified YOLOv4 as the most feasible model for deployment.  

Sign in / Sign up

Export Citation Format

Share Document