scholarly journals System design for passive human detection using principal components of the signal strength space

2013 ◽  
Vol 10 (1) ◽  
pp. 423-452 ◽  
Author(s):  
Bojan Mrazovac ◽  
Milan Bjelica ◽  
Dragan Kukolj ◽  
Branislav Todorovic ◽  
Sasa Vukosavljev
Keyword(s):  
Principal Components ◽  
Detection Method ◽  
Power Saving ◽  
Signal Strength ◽  
Human Detection ◽  
Detection Accuracy ◽  
Human Presence ◽  
Development Costs ◽  
Human Presence Detection ◽  
Radio Signals

In this article, device-free human presence detection method based on principal components analysis of the radio signal strength variations is proposed. The method increases user awareness for automated power management interaction in residential power saving systems. Motivation comes from a need for decreasing the installation complexity and development costs induced by the integration of specific human presence detection sensors. The method exploits the fact that a human body interferes with radio signals by introducing irregularities in the radio signature which indicate possible human presence. By analyzing radio signals between radio transceivers embedded in 2.4 GHz wireless power outlets, the original environment is not visually modified and a certain level of sensorial intelligence is introduced without additional sensors. The analysis of the signal strength variations in principal components space enhances the detection accuracy level of human presence detection method, retaining low installation costs and improving overall energy conservation.

scholarly journals A Novel Vehicle Detection Method Based on the Fusion of Radio Received Signal Strength and Geomagnetism

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 58 ◽  
Author(s):  
Liangliang Lou ◽  
Jinyi Zhang ◽  
Yong Xiong ◽  
Yanliang Jin
Keyword(s):  
Error Detection ◽  
Detection Method ◽  
Signal Strength ◽  
Vehicle Detection ◽  
Received Signal Strength ◽  
Detection Accuracy ◽  
Detection Problem ◽  
Traffic Light ◽  
Radio Stations ◽  
Multiple Detection

A geomagnetic signal blind zone exists between the front and rear axle of high-chassis vehicle such as trucks and buses, which leads to multiple-detection problem in detecting those vehicles running at low speed on roads or error-detection problem in the case of the stopping position of the vehicle is not standard when waiting for the traffic light to change. In order to improve the detection accuracy of any type of vehicle running at any speed, a novel two-sensors data fusion vehicle detection method through combining received signal strength from radio stations with geomagnetism around vehicles is designed and verified in the paper. Experimental results show that the accuracy of our proposed method can reach 95.4% and traditional single magnetism-based detection method was only 83.4% in the detection of high-chassis vehicles.

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.  

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

scholarly journals An Automatic Concrete Crack-Detection Method Fusing Point Clouds and Images Based on Improved Otsu’s Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1581
Author(s):  
Xiaolong Chen ◽  
Jian Li ◽  
Shuowen Huang ◽  
Hao Cui ◽  
Peirong Liu ◽  
...  
Keyword(s):  
Crack Detection ◽  
Detection Method ◽  
Multiple Scales ◽  
Point Clouds ◽  
Depth Image ◽  
Detection Accuracy ◽  
Detection Techniques ◽  
Concrete Crack ◽  
3D Point Clouds ◽  
2D Images

Cracks are one of the main distresses that occur on concrete surfaces. Traditional methods for detecting cracks based on two-dimensional (2D) images can be hampered by stains, shadows, and other artifacts, while various three-dimensional (3D) crack-detection techniques, using point clouds, are less affected in this regard but are limited by the measurement accuracy of the 3D laser scanner. In this study, we propose an automatic crack-detection method that fuses 3D point clouds and 2D images based on an improved Otsu algorithm, which consists of the following four major procedures. First, a high-precision registration of a depth image projected from 3D point clouds and 2D images is performed. Second, pixel-level image fusion is performed, which fuses the depth and gray information. Third, a rough crack image is obtained from the fusion image using the improved Otsu method. Finally, the connected domain labeling and morphological methods are used to finely extract the cracks. Experimentally, the proposed method was tested at multiple scales and with various types of concrete crack. The results demonstrate that the proposed method can achieve an average precision of 89.0%, recall of 84.8%, and F1 score of 86.7%, performing significantly better than the single image (average F1 score of 67.6%) and single point cloud (average F1 score of 76.0%) methods. Accordingly, the proposed method has high detection accuracy and universality, indicating its wide potential application as an automatic method for concrete-crack detection.

scholarly journals Examination of Abnormal Behavior Detection Based on Improved YOLOv3

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 197
Author(s):  
Meng-ting Fang ◽  
Zhong-ju Chen ◽  
Krzysztof Przystupa ◽  
Tao Li ◽  
Michal Majka ◽  
...  
Keyword(s):  
Detection Method ◽  
Abnormal Behavior ◽  
Detection Accuracy ◽  
Average Precision ◽  
Research Results ◽  
Behavior Detection ◽  
Abnormal Behavior Detection ◽  
The Mean ◽  
Examination Room ◽  
Detection Speed

Examination is a way to select talents, and a perfect invigilation strategy can improve the fairness of the examination. To realize the automatic detection of abnormal behavior in the examination room, the method based on the improved YOLOv3 (The third version of the You Only Look Once algorithm) algorithm is proposed. The YOLOv3 algorithm is improved by using the K-Means algorithm, GIoUloss, focal loss, and Darknet32. In addition, the frame-alternate dual-thread method is used to optimize the detection process. The research results show that the improved YOLOv3 algorithm can improve both the detection accuracy and detection speed. The frame-alternate dual-thread method can greatly increase the detection speed. The mean Average Precision (mAP) of the improved YOLOv3 algorithm on the test set reached 88.53%, and the detection speed reached 42 Frames Per Second (FPS) in the frame-alternate dual-thread detection method. The research results provide a certain reference for automated invigilation.

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
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