scholarly journals A Vision-Based Social Distancing and Critical Density Detection System for COVID-19

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4608
Author(s):  
Dongfang Yang ◽  
Ekim Yurtsever ◽  
Vishnu Renganathan ◽  
Keith A. Redmill ◽  
Ümit Özgüner
Keyword(s):  
Visual Cues ◽  
Detection System ◽  
Critical Density ◽  
Region Of Interest ◽  
Spatial Awareness ◽  
Real Time System ◽  
Effective Measure ◽  
Social Distancing ◽  
Record Data ◽  
Real World Datasets

Social distancing (SD) is an effective measure to prevent the spread of the infectious Coronavirus Disease 2019 (COVID-19). However, a lack of spatial awareness may cause unintentional violations of this new measure. Against this backdrop, we propose an active surveillance system to slow the spread of COVID-19 by warning individuals in a region-of-interest. Our contribution is twofold. First, we introduce a vision-based real-time system that can detect SD violations and send non-intrusive audio-visual cues using state-of-the-art deep-learning models. Second, we define a novel critical social density value and show that the chance of SD violation occurrence can be held near zero if the pedestrian density is kept under this value. The proposed system is also ethically fair: it does not record data nor target individuals, and no human supervisor is present during the operation. The proposed system was evaluated across real-world datasets.

scholarly journals Critical density for coverage and connectivity in non-orientation directional sensor network

2020 ◽  
Vol 16 (6) ◽  
pp. 155014772092575
Author(s):  
Lin Kang ◽  
Zengshou Dong ◽  
Yanjie Qi
Keyword(s):  
Sensor Network ◽  
Critical Density ◽  
Region Of Interest ◽  
Field Of View ◽  
Wireless Sensor ◽  
Connected Component ◽  
Simulation Results ◽  
Coverage And Connectivity ◽  
Directional Sensor Network ◽  
Directional Sensor

Both coverage and connectivity are important problems in wireless sensor networks. As more and more non-orientation sensors are continuously added into the region of interest, the size of covered component and connected component increases; at some point, the network can achieve an entire coverage and full connectivity after which the network percolates. In this article, we analyze the critical density in non-orientation directional sensor network in which the orientations of the sensors are random and the sensors are deployed according to the Poisson point process. We propose an approach to compute the critical density in such a network. A collaborating path is proposed with the sum of field-of-view angles of two collaborating sensors being π. Then a correlated model of non-orientation directional sensing sectors for percolation is proposed to solve the coverage and connectivity problems together. The numerical simulations confirm that percolation occurs on the estimated critical densities. It is worth mentioning that the theoretical analysis and simulation results give insights into the design of directional sensor network in practice.

Driver’s Drowsiness Behaviour Detection by Using PSO/DPSO Algorithm for Urban Road System

2018 ◽  
Vol 7 (3.27) ◽  
pp. 516
Author(s):  
J Sudhakar ◽  
S Srinivasan
Keyword(s):  
Detection System ◽  
State Level ◽  
The State ◽  
Driver Fatigue ◽  
Time System ◽  
Real Time System ◽  
Urban Road ◽  
Vehicle Accidents ◽  
Driver Drowsiness ◽  
The World

In recent years driver fatigue is one of the major causes for vehicle accidents in the world. A direct way of measuring driver fatigue is measuring the state of the driver drowsiness.  So it is very important to detect the drowsiness of the driver to save life and property. In our system, this aims to develop a prototype of drowsiness detection system. This system is a real time system which captures image continuously and measures the state of the eye according to the specified algorithm and gives warning if required. Though there are several methods for measuring the drowsiness but this approach is completely non-intrusive which does not affect the driver in any way, hence giving the exact condition of the driver. For detection of drowsiness the each closure value of eye is considered. So when the closure of eye exceeds a certain amount then the driver is identified to be sleepy. The entire system is implemented using PSO, DPSO and FODPSO algorithm and detection of drowsiness behaviour of driver different eye state level.  

scholarly journals Wearable Social Distancing Detection System

2020 ◽  
Author(s):  
F.A Ahmad Naqiyuddin ◽  
W. Mansor ◽  
N. M. Sallehuddin ◽  
M. N. S. Mohd Johari ◽  
M. A. S. Shazlan ◽  
...  
Keyword(s):  
Detection System ◽  
Social Distancing

COLLISION DETECTION FOR CLOTH SIMULATION USING BOUNDING SPHERE HIERARCHY

2015 ◽  
Vol 75 (2) ◽  
Author(s):  
Abdullah Bade ◽  
Ching Sue Ping ◽  
Siti Hasnah Tanalol
Keyword(s):  
Collision Detection ◽  
Detection System ◽  
Computational Cost ◽  
Detection Algorithm ◽  
Frame Rate ◽  
Generation Process ◽  
Cloth Simulation ◽  
Real Time System ◽  
Simple Mass ◽  
Mass Spring

For the past 2-decades, the challenges of collision detection on cloth simulation have attracted numerous researchers.  Simple mass spring model is used to model the cloth where the movement of the particles within the cloth was controlled by applying the Newton’s second law. After the modeling stage, implementation of the collision detection algorithm took place on cloth has been done. The collision detection technique used is bounding sphere hierarchy. Then, quad tree is being used to partitioning the bounding sphere and the collision search was based on the top-down approach. A prototype of the collision detection system is developed on cloth simulation and several experiments were conducted. Time taken for this system to be executed is around 235.258 milliseconds. Then the frame rate is at the average of 22 frames per second which is close to the real time system. Times taken for the collision detection system travels from root to nodes were 23 seconds. As a conclusion, the computational cost for bounding sphere hierarchy is much higher because the bounding sphere required more vertices for generation process, however the execution time for bounding sphere hierarchy is faster than the AABB hierarchy.  

scholarly journals Photoacoustic monitoring of angiogenesis predicts response to therapy in healing wounds

2021 ◽  
Author(s):  
Yash Mantri ◽  
Jason Tsujimoto ◽  
Brian Donovan ◽  
Christopher C. Fernandes ◽  
Pranav S. Garimella ◽  
...  
Keyword(s):  
Visual Cues ◽  
Chronic Wounds ◽  
Detection System ◽  
Imaging Modality ◽  
Treatment Strategies ◽  
Early Response ◽  
Response To Therapy ◽  
Critical Parameters ◽  
Current Standard ◽  
Major Health Problem

Chronic wounds are a major health problem that cause the medical infrastructure billions of dollars every year. Chronic wounds are often difficult to heal and cause significant discomfort. Although wound specialists have numerous therapeutic modalities at their disposal, tools that could 3D-map wound bed physiology and guide therapy do not exist. Visual cues are the current standard but are limited to surface assessment; clinicians rely on experience to predict response to therapy. Photoacoustic (PA) ultrasound (US) is a non-invasive, hybrid imaging modality that can solve these major limitations. PA relies on the contrast generated by hemoglobin in blood which allows it to map local angiogenesis, tissue perfusion and oxygen saturation - all critical parameters for wound healing. This work evaluates the use of PA-US to monitor angiogenesis and stratify patients responding vs. not-responding to therapy. We imaged 19 patients with 22 wounds once a week for at least three weeks. Our findings suggest that PA imaging directly visualizes angiogenesis. Patients responding to therapy showed clear signs of angiogenesis and an increased rate of PA increase (p = 0.002). These responders had a significant and negative correlation between PA intensity and wound size. Hypertension was correlated to impaired angiogenesis in non-responsive patients. The rate of PA increase and hence the rate of angiogenesis was able to predict healing times within 30 days from the start of monitoring (power = 88%, alpha = 0.05) This early response detection system could help inform management and treatment strategies while improving outcomes and reducing costs.

scholarly journals A real-time pedestrian detection system for safety applications

2021 ◽  
Author(s):  
Rinju Alice John
Keyword(s):  
Processing Speed ◽  
Detection System ◽  
Pedestrian Detection ◽  
Region Of Interest ◽  
Gaussian Mixture ◽  
Model Method ◽  
System A ◽  
And Performance ◽  
Performance Rate ◽  
Safety Applications

Nowadays, People are more distracted by their vulnerable devices, whenever they enter a cross road. As a result, a fatal accident or injury will occur. This motivated the need to implement a reliable pedestrian detection system. To optimize the system, a cross road scenario is considered where the driver is taking a right turn and a smart camera is used to capture consecutive pictures of the pedestrian. The consecutive frames are studied using Region Of Interest method and the Gaussian mixture model method. Once the detected pedestrian enters region of interest in less than 2 meters, a warning and automatic brake system is initiated to prevent the accident. Finally, the results of the proposed methods are compared based on the processing speed and performance rate of the Shape based detection technique (Wei Zhang, [12]). The performance rate was above 90% and processing speed was about 1 sec for the proposed methods.

scholarly journals A Vision-Based Machine Learning Method for Barrier Access Control Using Vehicle License Plate Authentication

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3578 ◽  
Author(s):  
Kh Tohidul Islam ◽  
Ram Gopal Raj ◽  
Syed Mohammed Shamsul Islam ◽  
Sudanthi Wijewickrema ◽  
Md Sazzad Hossain ◽  
...  
Keyword(s):  
Access Control ◽  
Real Time ◽  
Processing Time ◽  
Region Of Interest ◽  
License Plate ◽  
License Plate Recognition ◽  
Real Time System ◽  
Detection Stage ◽  
High Level ◽  
Two Stages

Automatic vehicle license plate recognition is an essential part of intelligent vehicle access control and monitoring systems. With the increasing number of vehicles, it is important that an effective real-time system for automated license plate recognition is developed. Computer vision techniques are typically used for this task. However, it remains a challenging problem, as both high accuracy and low processing time are required in such a system. Here, we propose a method for license plate recognition that seeks to find a balance between these two requirements. The proposed method consists of two stages: detection and recognition. In the detection stage, the image is processed so that a region of interest is identified. In the recognition stage, features are extracted from the region of interest using the histogram of oriented gradients method. These features are then used to train an artificial neural network to identify characters in the license plate. Experimental results show that the proposed method achieves a high level of accuracy as well as low processing time when compared to existing methods, indicating that it is suitable for real-time applications.

scholarly journals A Novel Deep Learning Approach for Anomaly Detection of Time Series Data

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhiwei Ji ◽  
Jiaheng Gong ◽  
Jiarui Feng
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Short Term Memory ◽  
Training Data ◽  
Series Data ◽  
Real Time System ◽  
Statistical Strategy ◽  
Anomaly Classification ◽  
Real World Datasets

Anomalies in time series, also called “discord,” are the abnormal subsequences. The occurrence of anomalies in time series may indicate that some faults or disease will occur soon. Therefore, development of novel computational approaches for anomaly detection (discord search) in time series is of great significance for state monitoring and early warning of real-time system. Previous studies show that many algorithms were successfully developed and were used for anomaly classification, e.g., health monitoring, traffic detection, and intrusion detection. However, the anomaly detection of time series was not well studied. In this paper, we proposed a long short-term memory- (LSTM-) based anomaly detection method (LSTMAD) for discord search from univariate time series data. LSTMAD learns the structural features from normal (nonanomalous) training data and then performs anomaly detection via a statistical strategy based on the prediction error for observed data. In our experimental evaluation using public ECG datasets and real-world datasets, LSTMAD detects anomalies more accurately than other existing approaches in comparison.

scholarly journals Development of an Automatic Image Cropping and Feature Extraction System for Real-Time Warping Monitoring in 3D Printing

2021 ◽  
Author(s):  
Jiarui Xie
Keyword(s):  
Real Time ◽  
Detection System ◽  
Region Of Interest ◽  
Cost Effective ◽  
Time Warping ◽  
Time Data ◽  
Fused Filament Fabrication ◽  
System A ◽  
Effective Manner

Fused Filament Fabrication (FFF) is an additive manufacturing technology that can produce complicated structures in a simple-to-use and cost-effective manner. Although promising, the technology is prone to defects, e.g. warping, compromising the quality of the manufactured component. To avoid the adverse effects caused by warping, this thesis utilizes deep-learning algorithms to develop a warping detection system using Convolutional Neural Networks (CNN). To create such a system, a real-time data acquisition and analysis pipeline is laid out. The system is responsible for capturing a snapshot of the print layer-bylayer and simultaneously extracting the corners of the component. The extracted region-of-interest is then passed through a CNN outputting the probability of a corner being warped. If a warp is detected, a signal is sent to pause the print, thereby creating a closed-loop monitoring system. The underlying model is tested on a real-time manufacturing environment yielding a mean accuracy of 99.21%.

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