Study to Find Optimal Solution for Multi-objects Detection by Background Image Subtraction with CNN in Real-Time Surveillance System

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.

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.

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.

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.

The engineering of computer vision systems that meet application specific computational and accuracy requirements is crucial to the deployment of real-life computer vision systems. This paper illustrates how past work on a systematic engineering methodology for vision systems performance characterization can be used to develop a real-time people detection and zooming system to meet given application requirements. We illustrate that by judiciously choosing the system modules and performing a careful analysis of the influence of various tuning parameters on the system it is possible to: perform proper statistical inference, automatically set control parameters and quantify limits of a dual-camera real-time video surveillance system. The goal of the system is to continuously provide a high resolution zoomed-in image of a person's head at any location of the monitored area. An omni-directional camera video is processed to detect people and to precisely control a high resolution foveal camera, which has pan, tilt and zoom capabilities. The pan and tilt parameters of the foveal camera and its uncertainties are shown to be functions of the underlying geometry, lighting conditions, background color/contrast, relative position of the person with respect to both cameras as well as sensor noise and calibration errors. The uncertainty in the estimates is used to adaptively estimate the zoom parameter that guarantees with a user specified probability, /spl alpha/, that the detected person's face is contained and zoomed within the image.

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.

Multi-camera surveillance systems generally are used by intelligent vision applications to analysis moving objects in the urban environments. Object tracking is an essential component of these applications. Real-time tracking of moving objects in the crowded urban scenes is a challenging problem because the number of moving objects in the scene varies and is usually unpredictable. In order to meet the real-time requirements of intelligent vision applications, it is necessary to control the surveillance workload over processing system and enforce the utilization bound on the system. The utilization control is challenging especially when the workload in the system is unpredictable. The workload for a particular camera depending on the number of targets in its view and execution rate of services that are used to detect and track the moving objects. In this paper, an adaptive real-time system based on the feedback-control framework is proposed to control the system utilization by adjusting input video frame rate that guarantees real-time performance and quality of intelligent vision applications. This approach can handle the workload uncertainties by identifying the parameters of system model online by solving an optimization problem and can simultaneously adjust the frame rate in each sampling period. In each sampling instance, the system utilization is monitored and the error is obtained by comparing it with the set point. The real-time operation of the computing system is achieved by adjusting the input video frame rate to keep the utilization at a given set point slightly below their schedulable bound. Evaluation results demonstrate proposed scheme outperforms the existing schemes especially under workload uncertainties.

- 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.

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...

Surveillance systems are omnipresent in our day-to-day life as these systems ensure our safety, most of the surveillance systems are based on video sensory modality and machine intelligence. Video surveillance systems in real time are very expensive because they require very high processing. Most of the times, these lack reliability and robustness in real-time systems. To confront this issue, we have come up with real-time audio surveillance system. Where the audio signals are picked up using highly sensitive microphones and then the signal is amplified to a great extent. The picked-up signals contain a lot of other noise apart from the desired information. Hence, the procured audio signals are denoised with the help of signal processing technique called spectral subtraction. After denoising, there are chances of the mixing of audio signals so we are applying the speech separation techniques to get separate speech signals from the multi-speaker audio signal, which will help us to get clear insights of what a monitored person is speaking about.

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

Real time object tracking is an essential part of surveillance and robot applications. The performance of any object tracking system depends on its accuracy and its ability to deal with various sizes of objects, for better results the tracking should happen at a high speed. Object tracking can be defined as the process of segmenting the region of interest from the video sequence and keeping track of the motion in order to extract useful information. The basic fundamentals of video surveillance such as object detection, tracking and recognition using multiple cameras. Whenever an object enters a video frame, it is detected using background subtraction and the detected objects are tracked with a novel Bayesian Kalman filter with simplified Gaussian mixture (BKF-SGM). Object recognition algorithm is applied using Gait method which is employed to support multiple object tracking with its robust performance. Processing of image is done in MATLAB to get detection, tracking and recognized results.

in order to solve the adverse effects of strong light and shadow on the test results, a fusion frame difference and background subtraction method in the HSV space is used in this paper. By using frame difference method to solve the effect of strong light, but frame difference method can not detect object when the object do not move, the method of background subtraction can detect it, building Gaussian background model in the HSV space can eliminate shadows. Empirical results show that the method of fusion frame difference and background subtraction in the HSV space can get overcome the effect of strong light and shadows. Fusion background subtraction and frame difference method based on establishing a Gaussian mixture model in HSV space can overcome the disadvantages of the frame difference method, at the same time it can also solve the false detection of object which result from the background subtraction method.

In real time object tracking is an important task in various surveillance application. Nowadays, surveillance system very common in offices, collage campus, shopping malls etc. In this paper, an automated video surveillance system presented. Althrough tracking and counting system are commercially available today; there is need for further reaserach to address the challenges of real world scenarios. There is lot of surveillance cameras installed around us but there are no means to monitor all of them continuously. It is necessary to develop a computer vision based technologies that automatically process those images in order to detect situations or unusual behavior. In order to solve the problem of poor real time tracking performance using convolutional neural network. This paper proposes a fast and accurate real time video detection and tracking algorithm. Keyword: - Video surveillance system, Moving object detection, Tracking, Background subtraction. I. Introduction Real time human body detection and tracking in indoor environment deals with the concept of higher level application of image processing and computer vision. The following key components of identify to path of real time tracking are extracting a feature, background image subtraction. If two similar color objects come to close the tracker of one object can jump to associated object. The problem is called hijacking problem. Drifting problem occur when an object abruptly changes its direction to reverse. In that case it’s become very difficult to track the object because the motion model does not work. An object tracking can be understood as the problem of finding the path (i.e. Trajectory).

This paper presents a novel distributed intelligent video surveillance architecture based on Wireless Multimedia Sensor Networks (WMSNs). This architecture is part of a video surveillance project and has been built using the Robot Operating System (ROS). ROS allows to develop and connect (through wireless TCP-IP) several modules to process and manage multimedia data in an easy way. The real-time intelligent surveillance system has been trained for detecting, tracking and monitoring people and vehicles in an indoor-outdoor real environment. The test process shows the reliability of the developed system as a tool for the identification of security incidents. Besides, using wireless connections and a distributed architecture together, we have achieved a really flexible, easy to install and lower-maintenance system that supports many different devices. Thus, the proposed architecture can be applied in distributed locations such as smart cities.