A Review of Microwave Wireless Techniques for Human Presence Detection and Classification

2017 ◽  
Vol 65 (5) ◽  
pp. 1780-1794 ◽  
Author(s):  
Jeffrey A. Nanzer
Keyword(s):  
Human Presence ◽  
Human Presence Detection

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.  

Controlling Led by Voice Command, Timer and Human Presence Detection

2021 ◽  
Vol 9 (8) ◽  
pp. 2143-2150
Author(s):  
Vrushali M. Lingayat
Keyword(s):  
Mobile App ◽  
Home Automation ◽  
Light Weight ◽  
Motion Detector ◽  
Human Presence ◽  
Voice Command ◽  
Human Presence Detection

Abstract: This paper focuses on the concept of home automation using arduino kit via mobile app. This paper describes and gives the direction for Arduino controlled light-weight bulb through PIR motion detector, voice commands as well as using buttons. We tend to use the PIR motion detector to get the presence of somebody's body to ON the bulb keep with the output of the motion detector. This can be used in very little areas like storerooms. We are in a position to place the motion detector at the door of house and once someone can enter the room, the motion detector will discover it and light-weight will get ON. The PIR motion detector can discover the shape up to seven meters so it's sensible for little rooms. This project additionally permits light automation by sensing the human presence inside the required varies.

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