Statistical modeling and performance characterization of a real-time dual camera surveillance system

Now a day, video surveillance has gained the most attention in real-time applications. Because of the improvement in domain of machine learning, various methodologies have been designed for multi-object detection. The real-time surveillance systems are the need of various organization as security; hence, this area of research has important. This paper introduces a new methodology for Moving object detection. In the area of real-time video surveillance systems, multi-object detection and object tracking are an important step in video processing. The moving system is critical for many of the application such as operational robot, surveillance systems in military. These real-time surveillance systems are more useful for intelligence gathering, security issues, and many personal needs. Different techniques are used for this task and research is vastly done to make this system automated and to make it reliable. In this research, subjective quality assessment of object detection and object tracking is discussed in detail. In the proposed system, the background subtraction is done from the clean original image and use of CNN with this. We have applied background subtraction method with different filtering algorithms. We have implemented and tried MoD2 and KNN algorithm on the test video and fetched the foreground image. After this, we have applied CNN to track the object. After tracking the objects, we have found out the accuracy with background subtraction method and without background subtraction method. The proposed system improved by approximately 2% as compared to existing techniques.

Objective Some countries have implemented systems to monitor suicides in real-time. These systems differ because of the various ways in which suicides are identified and recorded. The main objective of this study was to conduct an international comparison of major real-time suicide mortality surveillance systems to identify joint strengths, challenges, and differences, and thereby inform best-practice criteria at local, national, and international levels. Methods Five major real-time suicide mortality surveillance systems of various coverage levels were identified and selected for review via an internet-based scoping exercise and prior knowledge of existing systems. Key information including the system components and practices was collated from those organizations that developed and operate each system using a structured template. The information was narratively and critically synthesized to determine similarities and differences between the systems. Results The comparative review of the five established real-time suicide surveillance systems revealed more commonalities than differences overall. Commonalities included rapid, routine surveillance based on minimal, provisional data to facilitate timely intervention and postvention efforts. Identified differences include the timeliness of case submission and system infrastructure. Conclusion The recommended criteria could promote replicable components and practices in real-time suicide surveillance while offering flexibility in adapting to regional/local circumstances and resource availability. HIGHLIGHTS Evidence-informed recommendations for current best practice in real-time suicide surveillance. Proposed comprehensive framework can be adapted based on available resources and capacity. Real-time suicide mortality data facilitates rapid data-driven decision-making in suicide prevention.

- Managing crowd safety during the Kumbh Mela presents unprecedented logistical challenges due to its scale,density, and dynamic environment. This work introduces Kumbh-Connect, a real-time intelligent surveillance system that leverages computer vision and machine learning to enhance public safety.The framework combines YOLOv11n for person and weapon detection, ByteTrack for movement analysis, and DeepFace form facial recognition of missing individuals. A FastAPI backend processes video streams from CCTV feeds, while WebSockets enable instant alerts to security personnel via a mobile application.Testing on simulated event environments shows 90% accuracy in crowd estimation and 92% in facial matching. Designed for scalability, KumbhConnect offers a proactive approach to crowd control, reducing response time and improving situational awareness during large-scale gatherings. Index Terms—Kumbh Mela, Crowd Monitoring, Object Detection, Facial Recognition, Real-Time Surveillance, YOLO.

In September 2004, Kingston, Frontenac, Lennox and Addington (KFL and A) Public Health, in collaboration with the Public Health Division of the Ontario Ministry of Health and Long-Term Care, Queen's University, the Public Health Agency of Canada, Kingston General Hospital and Hotel Dieu Hospital, began a 2-year pilot project to implement and evaluate an emergency department (ED) chief complaint syndromic surveillance system. Our objective was to evaluate a comprehensive and readily deployable real-time regional syndromic surveillance program and to determine its ability to detect gastrointestinal or respiratory outbreaks well in advance of traditional reporting systems. In order to implement the system, modifications were made to the University of Pittsburgh's Real-time Outbreak and Disease Surveillance (RODS) system, which has been successfully integrated into public health systems, and has enhanced communication and collaboration between them and EDs. This paper provides an overview of a RODS-based syndromic surveillance system as adapted for use at a public health unit in Kingston, Ontario. We summarize the technical specifications, privacy and security considerations, data capture, classification and management of the data streams, alerting and public health response. We hope that the modifications described here, including the addition of unique data streams, will provide a benchmark for future Canadian syndromic surveillance systems.

Surveillance épidémiologique en temps réel dans les armées ; concepts, réalités et perspectives en France

In recent years, especially in industrial automation systems, in order for robots to understand their distances and positions according to the target, close to human vision, computer vision systems are needed. Computer vision close to human vision can only be created using stereo cameras. In this study, a computer vision system is developed using the stereo camera system for measuring object distances. In the study, first of all, for the distance measurement, the distance of the face images obtained from the stereo camera system to the screen is calculated. In measuring the distances of the face images to screen, the disparity maps are first extracted and the face region is detected. Afterwards, the distance measurements are performed on the obtained images in the stereo camera system on account of calculating the shifts between the frames. In the experimental studies, the actual distance values such as 71, 74, 75, 79, 110, 125, and 115 of the face to the screen are measured as 70, 72, 73, 77, 97, 120, 132 cm by proposed system, respectively. When the experimental results are examined, we can say that the proposed computer vision system is successful in distance measurement using stereo camera view.

Rabies is a neglected viral zoonotic disease with an almost 100% fatality rate. A pilot project was initiated by the National Institute of Health (NIH), Islamabad, in collaboration with Health Security Partners (HSP) to establish a real-time dog bite surveillance in humans in public sector secondary care hospitals of Islamabad. The main objective of this study was to analyze different characteristics of dog bite cases, identify gaps in surveillance, and recommend suggestions to improve and strengthen real-time dog bite surveillance systems. An observational cross-sectional study was conducted in two major public sector hospitals in Islamabad from December 2019 to July 2020. Data on demographic information, site of the dog bite, category of the dog bite, and treatment was collected via a WVS mobile-based application. Descriptive analysis was carried out for different variables at a p-value of <0.05. A total of 338 dog bite cases were captured in the World Veterinary Service (WVS) application, and most cases (n = 226, 85.6%) were reported in December 2019 with a male-to-female ratio of 2:1. Most cases were reported from the age group of 22 to 31 years (n = 178, 53%). Out of the total number of cases, 263 (78%) presented with a single anatomical location, and 174 (51%) had Category II wounds. All dog bite patients were given only the first dose of the anti-rabies vaccine at the hospital level. Real-time dog bite surveillance via a mobile-based application proved to be effective for the timely recording and management of dog bite cases. Young people were reported to be mostly affected by dog bites, and nearly half of the cases were managed in hospitals. Refresher training was conducted for medics and paramedics for mobile-based applications, dog bite management, and proper referral of cases to tertiary care hospitals.

In this paper, we propose detection based simplified multiple object tracking model with handling stationary object detection and occlusion problem for real-time intelligent surveillance system. In order to solve detection of slow and stationary object problem in Gaussian Mixture Model(GMM) based adaptive background model, we presents controlling learning rate mechanism using tracked region information. And, the simple primitive multi-features are applied for real-time multiple object tracking. As well, we proposed modified moving average filter for predicting next position of moving object to handle occlusion problems. Computational and real-target experiment results show that the proposed model can successfully track moving object within 45ms per frame for 640×480 image size on Intel® Core(TM) i7 CPU 1.6GHz in a real indoor scene including occlusion situation.

BackgroundIn a climate of concern over bioterrorism threats and emergent diseases, public health authorities are trialling more timely surveillance systems. The 2003 Rugby World Cup (RWC) provided an opportunity to test the viability of a near real-time syndromic surveillance system in metropolitan Sydney, Australia. We describe the development and early results of this largely automated system that used data routinely collected in Emergency Departments (EDs).MethodsTwelve of 49 EDs in the Sydney metropolitan area automatically transmitted surveillance data from their existing information systems to a central database in near real-time. Information captured for each ED visit included patient demographic details, presenting problem and nursing assessment entered as free-text at triage time, physician-assigned provisional diagnosis codes, and status at departure from the ED. Both diagnoses from the EDs and triage text were used to assign syndrome categories. The text information was automatically classified into one or more of 26 syndrome categories using automated "naïve Bayes" text categorisation techniques. Automated processes were used to analyse both diagnosis and free text-based syndrome data and to produce web-based statistical summaries for daily review. An adjusted cumulative sum (cusum) was used to assess the statistical significance of trends.ResultsDuring the RWC the system did not identify any major public health threats associated with the tournament, mass gatherings or the influx of visitors. This was consistent with evidence from other sources, although two known outbreaks were already in progress before the tournament. Limited baseline in early monitoring prevented the system from automatically identifying these ongoing outbreaks. Data capture was invisible to clinical staff in EDs and did not add to their workload.ConclusionWe have demonstrated the feasibility and potential utility of syndromic surveillance using routinely collected data from ED information systems. Key features of our system are its nil impact on clinical staff, and its use of statistical methods to assign syndrome categories based on clinical free text information. The system is ongoing, and has expanded to cover 30 EDs. Results of formal evaluations of both the technical efficiency and the public health impacts of the system will be described subsequently.

Statement of PurposeIn 2015, a record number of Americans died of an opioid-involved overdose, bringing devastation to families and communities in urban and rural communities alike. Now, more people in...

Sykdomspulsen is a real time surveillance system developed by the Norwegian Institute of Public Health (NIPH) for One Health surveillance and the surveillance of other infectious diseases in humans like respiratory diseases and lately covid-19. The One Health surveillance comprise of Campylobacter data from humans and chicken farms and also includes diagnosis codes from doctor appointments and weather data with analysis forecasting outbreaks in Norway. It is a joint project between the Norwegian Institute of Public Health (NIPH) and the Norwegian Veterinary Institute (NVI), under the framework of the OHEJP NOVA (Novel approaches for design and evaluation of cost-effective surveillance across the food chain) and MATRIX (Connecting dimensions in One-Health surveillance) projects. The system relies on two pillars, the first being an analytics infrastructure which in real time retrieves data from tens of sources, cleans and harmonizes it, then runs over half a million analyses each day and produces over 20 000 000 rows of results to be used every day. The analytics infrastructure is based on R. Results are notably being used by NIPH for the monitoring of covid-19 development and the surveillance of other transmittable diseases such as influenza and gastro-intestinal illness. The analytics framework also generates hundreds of reports every day, directed at dissemination to municipal health authorities. This framework is not currently publicly available, but an open-source release is expected by the end of 2021. The second pilar is an interactive R Shiny dashboard platform, which is used for communicating the data and the model results to partner organisations. It allows for the easy creation of a website where public and animal health researchers and food safety experts can view real time analyses. This dashboard combines the powerful data visualisation and analysis strength of R with the accessibility, flexibility, structure and interactivity of web-based platforms. The result is a real time interactive surveillance system, that is supported by a solid infrastructure and streamlined data flow, and shared with actors through a beautiful and user-friendly website, based entirely on R.

AimTo investigate primary care clinicians’ views of a prototype locally relevant, real-time viral surveillance system to assist diagnostic decision-making and antibiotic prescribing for paediatric respiratory tract infections (RTI). Clinicians’ perspectives...

Unmanned Aerial Vehicles (UAVs) have gained significant traction for real-time surveillance applications such as object tracking and search-and-rescue missions. A key enabler of these capabilities is the integration of a real-time, highly accurate object detection system. While available two-stage detectors like R-CNN, Fast R-CNN, Faster R-CNN, and Mask R-CNN achieve high detection accuracy and precise localization by dividing the image into regions and classifying each region, they are often slow, complex, and resource-intensive, making them unsuitable for real-time applications. To address this limitation, this research utilizes a Darknet-based YOLOv3 (You Only Look Once, Version 3), a state-of-the-art algorithm for real-time object detection in videos, live feeds, and images. Leveraging a deep Convolutional Neural Network (CNN), YOLOv3 efficiently learns features and predicts object locations and class probabilities in a single pass, ensuring high-speed and reliable detection. A real-time aerial surveillance system for quadcopter application using machine vision is proposed. The training dataset, obtained from the internet and self-taken images from a camera, was manually annotated into three critical categories: suspect, bandit, and weapon. The dataset was subsequently divided into training and testing subsets. Experimental results demonstrate that the proposed system achieves outstanding detection accuracy, with an overall mean average precision (mAP@0.5) of 93.82%, precision of 94%, and recall of 83%. Compared to a ResNet-50-based Faster R-CNN model, the YOLOv3-based approach outperformed, achieving success rates of 0.81 for the Bandit class and 1.00 for both Suspect and Weapon classes. This research improves drone-based AI for real-time object detection in security systems, enhancing efficiency and adaptability. It also creates specialized datasets, including a tailored bandit dataset, to support future UAV security research.

BackgroundRespiratory syncytial virus (RSV) surged in the 2022–2023 respiratory season after low activity during the pandemic. To monitor the RSV season in real time and support healthcare planning, Ontario introduced daily hospital bed census reporting of RSV hospitalizations by age group (0–17, 18–64, 65 years and older).ObjectivesTo assess the completeness and quality of the newly introduced real-time surveillance compared to end-of-season ICD-10 coded hospitalization discharge abstract data (DAD) from November 22, 2022, to March 31, 2023.MethodsRespiratory syncytial virus hospitalizations from both data sources were compared to RSV laboratory positivity to assess concordance with overall RSV activity. A longitudinal comparison by age group was assessed by time-lagged cross-correlation of the daily submission data versus DAD data, including cross correlation coefficients for each time lag, confidence bound and the highest correlation value.ResultsBoth data sources followed trends in RSV positivity. Data by age groups showed an early peak of paediatric admissions followed by a peak in adult and older adult hospitalizations. Daily surveillance consistently underestimated hospitalizations with a peak of 430 beds by DAD on January 7, 2023, versus 322 beds (75%) for daily reporting on the same day. The maximum correlation coefficient values were 0.67 (all ages), 0.57 (0–17 years), 0.66 (18–64 years) and 0.63 (65 years and older).ConclusionImplementation of daily hospital reporting provided accurate trending in RSV hospitalizations by age group to inform within season healthcare and public health planning.

Some characteristics of flames, including vague shapes and fire-like colors, are irregular, so it is difficult to use a video camera for real-time flame detection. To achieve fully automatic surveillance of fires, a real-time incipient flame detection approach that uses three stages of video processing is proposed in this study. Firstly, a thumbnail method is applied to the flame image for the various resolutions for real-time processing. A median filter and a Gaussian filter are used to remove the image noise. The flame edge information in the image is then enhanced, using a sharpening filter. Secondly, quasi-periodic behavior in flame boundaries is detected using motion detection and a background edge model, so some candidates for real flames can be selected. Thirdly, an evaluation method that uses four criteria, including compactness, fill rate, corner flicker rate and growth rate is used to identify the correct flame. The results are verified using videos of complex scenes at different resolutions. A comparison with other systems is made, to demonstrate that the proposed method is more accurate.