Poster: Monash Research Month 2009
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The document describes an intelligent security application being developed to detect unusual events from surveillance video streams. It uses computer vision and artificial intelligence techniques like foreground region detection, feature extraction and temporal feature analysis to help human monitors take early action against malicious events. The application separates foreground regions using adaptive background modeling, extracts scene-level features from those regions, and analyzes temporal variations in the features to map them to high-level event models.
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![Intelligent Security
Application for Unusual Event Detection
Mahfuzul Haque, Manzur Murshed, and Manoranjan Paul
Gippsland School of Information Technology, Monash University, Victoria 3842, Australia
Email: {Mahfuzul.Haque, Manzur.Murshed, Manoranjan.Paul}@infotech.monash.edu.au
1
Abstract
The goal of this research project is to develop intelligent software that can see and detect unusual events from
surveillance video stream. As the growing number of security camera is challenging the reliability of existing security
systems which are still relying on human monitors, this project exploits computer vision and artificial intelligence for
developing intelligent security application that can aid human monitors taking early actions against malicious events.
2
Motivation
5
Event Detection
Frame # 1
6
Frame # 75
Frame # 150
Frame # 225
Frame # 300
Performance Evaluation
First Frame
Test Frame
Ideal Result
Actual Result
Image source: http://www.alert-sec.co.uk/Images/control_room.jpg
Security control room: too many cameras, too many screens!
3
Foreground Region Detection
Background
Model
Current Scene
Foreground Region
First, active foreground regions are separated using an adaptive model of
the scene background [1][2], and then scene level features are extracted
from those regions.
4
7
The Application
8
References
Temporal Feature Analysis
Total Foreground Area (%)
Frame # 1
Frame # 75
Frame # 150
Frame # 225
Frame # 300
Total 270 temporal features are computed
from the time varying scene level features.
Finally, top ranked features are mapped to
high level event models.
Observed Data
Smoothed Data
Time
[1] Mahfuzul Haque, Manzur Murshed, and Manoranjan Paul, On Stable Dynamic Background
Generation Technique using Gaussian Mixture Models for Robust Object Detection, IEEE
International Conference On Advanced Video and Signal Based Surveillance (AVSS), New Mexico,
USA, 2008.
[2] Mahfuzul Haque, Manzur Murshed, and Manoranjan Paul, Improved Gaussian Mixtures for
Robust Object Detection by Adaptive Multi-Background Generation, International Conference
on Pattern Recognition (ICPR), Tampa, Florida, USA, 2008.](https://image.slidesharecdn.com/poster-monash-research-month-2009-131229013009-phpapp02/85/Poster-Monash-Research-Month-2009-1-320.jpg)
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Vitamins B1, B2, B3, and B5 are important cofactors for enzymes involved in cellular metabolism. Vitamin B1 or thiamine is a cofactor for enzymes in carbohydrate metabolism. Vitamin B2 or riboflavin is a precursor for FAD and FMN which are electron carriers in the electron transport chain and metabolic pathways like the citric acid cycle. Vitamin B3 or niacin is needed to make NAD+, which is used in glycolysis and the electron transport chain. Vitamin B5 or pantothenic acid is needed to make Coenzyme A, which is involved in acetyl group transfers in metabolic pathways.
Talk by Lindsey Collumbell to Junior PR professionals, 13th May 2015
Talk by Lindsey Collumbell to Junior PR professionals, 13th May 2015Lindsey Collumbell ChartPR MCIPR CMktr MCIM
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Oss 2009- How Open Source Software Can Save the ICT Industry
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20131229 주일예배, 사61장01 11절, 하나님의 은혜의 해 요약본
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Talk by Lindsey Collumbell to Junior PR professionals, 13th May 2015
Talk by Lindsey Collumbell to Junior PR professionals, 13th May 2015
Similar to Poster: Monash Research Month 2009
ATM Security System Based on the Video Surveillance Using Neural Networks
This document proposes an ATM security system based on video surveillance using neural networks. The system uses computer vision and deep learning techniques to monitor ATM activity and detect unusual events like multiple people entering, cameras being removed, or people with obscured faces. Video frames are analyzed to identify objects and detect abnormal behaviors. If anything unusual is detected, an alert will be sent immediately to the nearest station and the ATM doors will lock. The system is intended to provide real-time security monitoring at low cost as an improvement over relying solely on human security guards. It describes the methodology used, including video acquisition, frame conversion, preprocessing, background modeling, feature extraction using algorithms like convolutional neural networks to identify faces and detect abnormal situations.
Cyber threat detection by siem tools
This document discusses using a SIEM (Security Information and Event Management) solution to detect cyber threats in real-time or from historical data. It describes setting up a SIEM solution in a home lab to integrate various components that provide security information and event management. The goal is to discover the best way to detect threats on networks using artificially intelligent SIEM tools by analyzing data logs and enabling threat hunting and forensics.
Smart surveillance using deep learning
Smart surveillance systems play an important role in security today. The goal of security systems is to protect users against fires, car accidents, and
other forms of violence. The primary function of these systems is to offer security in residential areas. In today’s culture, protecting our homes is
critical. Surveillance, which ranges from private houses to large corporations, is critical in making us feel safe. There are numerous machine learning algorithms for home security systems; however, the deep learning convolutional neural network (CNN) technique outperforms the others. The
Keras, Tensorflow, Cv2, Glob, Imutils, and PIL libraries are used to train and assess the detection method. A web application is used to provide a
user-friendly environment. The flask web framework is used to construct it. The flash-mail, requests, and telegram application programming interface (API) apps are used in the alerting approach. The surveillance system tracks
abnormal activities and uses machine learning to determine if the scenario is normal or not based on the acquired image. After capturing the image, it is
compared with the existing dataset, and the model is trained using normal events. When there is an anomalous event, the model produces an output from which the mean distance for each frame is calculated.
Poster: Monash Research Month 2008
This document presents an intelligent surveillance framework for real-time abnormal behavior recognition from video streams. The framework uses background modeling and Gaussian mixture models to detect foreground regions and track object movements. It then analyzes the detected behaviors to identify any unusual or malicious group activities. The goal is to help human monitors more reliably identify security threats by automating some detection tasks.
DEEP LEARNING APPROACH FOR SUSPICIOUS ACTIVITY DETECTION FROM SURVEILLANCE VIDEO
This document proposes using deep learning approaches like convolutional neural networks to detect suspicious activities from surveillance videos. It aims to overcome limitations of existing depth sensors by using neural networks for more accurate human pose estimation. The system would monitor public areas through video surveillance and categorize human activities as usual or suspicious in real-time. It addresses gaps in previous research which focused on images rather than videos. The proposed methodology includes collecting and preprocessing surveillance video data, designing and training deep learning models to recognize and classify activities, and integrating the system with existing surveillance infrastructure for deployment.
Detecting anomalies in security cameras with 3D-convolutional neural network ...
This paper presents a novel deep learning-based approach for anomaly detec- tion in surveillance films. A deep network that has been trained to recognize objects and human activity in movies forms the foundation of the suggested ap- proach. In order to detect anomalies in surveillance films, the proposed method combines the strengths of 3D-convolutional neural network (3DCNN) and con- volutional long short-term memory (ConvLSTM). From the video frames, the 3DCNN is utilized to extract spatiotemporal features,while ConvLSTM is em- ployed to record temporal relationships between frames. The technique was evaluated on five large-scale datasets from the actual world (UCFCrime, XD- Violence, UBIFights, CCTVFights, UCF101) that had both indoor and outdoor video clips as well as synthetic datasets with a range of object shapes, sizes, and behaviors. The results further demonstrate that combining 3DCNN with Con- vLSTM can increase precision and reduce false positives, achieving a high ac- curacy and area under the receiver operating characteristic-area under the curve (ROC-AUC) in both indoor and outdoor scenarios when compared to cutting- edge techniques mentioned in the comparison.
Weapon detection using artificial intelligence and deep learning for security...
This document discusses using artificial intelligence and deep learning for weapon detection in security applications. It proposes detecting guns and weapons from images using convolutional neural network models like SSD and Faster RCNN. The system takes video frames as input, applies frame differencing to detect changes, and uses deep learning models to classify if objects in the frames are weapons or not. The goal is accurate and fast weapon detection to improve security monitoring.
SMART SURVEILLANCE SYSTEM USING LBPH ALGORITHM
The document describes a proposed smart surveillance system using the LBPH algorithm and structural similarity index measure (SSIM). The system uses LBPH for face recognition as an authentication procedure when an unknown face is detected. It also uses SSIM to detect differences between frames and identify objects that have been removed from the surveillance area. The system aims to provide intelligent CCTV that can not only record video but also process video in real-time to detect unwanted persons entering the surveillance area. It discusses the algorithms used, including LBPH, SSIM, and a literature review of related work on smart surveillance systems.
NoxEye.pptx
NoxEye is an AI-based threat detection system for real-time video surveillance. It uses Faster RCNN models running on fog nodes to detect and label possible crime events and objects in motion-captured images from edge nodes. When a crime is detected, the images are saved and notifications are sent to police. This provides efficient, real-time crime detection while distributing processing load across the IoT/fog/cloud architecture. The system aims to prevent crimes with high prediction accuracy and fast response times through an easy-to-use interface.
IRJET - Real-Time Analysis of Video Surveillance using Machine Learning a...
This document discusses a proposed real-time video surveillance system that utilizes machine learning, computer vision, and image processing algorithms. The system aims to detect and analyze objects of interest in CCTV footage in order to identify suspicious activities and assist authorities. It employs algorithms for face detection and recognition, as well as detection of weapons and abnormal movements. The system uses frameworks like OpenCV and TensorFlow to perform tasks like facial analysis, age and gender estimation, human pose estimation, and weapon detection in real-time video streams. It analyzes existing algorithms and evaluates their suitability for the system. The results of implementing and testing various algorithms on sample footage are also presented.
Optimizing cybersecurity incident response decisions using deep reinforcemen...
The main purpose of this paper is to explore and investigate the role of deep reinforcement learning (DRL) in optimizing the post-alert incident response process in security incident and event management (SIEM) systems. Although machine learning is used at multiple levels of SIEM systems, the last mile decision process is often ignored. Few papers reported efforts regarding the use of DRL to improve the post-alert decision and incident response processes. All the reported efforts applied only shallow (traditional) machine learning approaches to solve the problem. This paper explores the possibility of solving the problem using DRL approaches. The main attraction of DRL models is their ability to make accurate decisions based on live streams of data without the need for prior training, and they proved to be very successful in other fields of applications. Using standard datasets, a number of experiments have been conducted using different DRL configurations The results showed that DRL models can provide highly accurate decisions without the need for prior training.
Whitepaper perimeter protection
Network video surveillance offers versatile perimeter protection that allows property owners to monitor their premises remotely and identify real security threats. Thermal network cameras can detect intruders day or night without requiring light, and trigger PTZ cameras to provide high-resolution video for verification. Even small, automated systems using video analytics can effectively protect perimeters in a scalable and cost-efficient way, sending real-time alerts while reducing false alarms. Network video surveillance enables early detection of intrusions as well as verification of security incidents, distinguishing real threats from false alarms.
Motion Detection System for Security Using IoT- Survey
This document discusses a survey of motion detection systems for security using IoT. It begins with an abstract describing how intrusion detection systems with alarm systems have become necessary due to frequent burglaries. It then discusses how motion can be detected using devices that detect changes in speed, vector, or environment. The project discussed uses a PIR sensor coupled with a microcontroller to detect intruder motion and activate an alarm. It also reviews related works involving using sensor networks and deep learning models for motion detection, comparing performance of different motion detectors, PIR sensor-based security systems, and adaptive background removal for motion detection in video surveillance. It concludes that a modular home security system communicating via phone call could be useful for homes, businesses, and
IRJET- Smart and Ubiquitous Defense Monitoring System based on IoT
This document describes a smart and ubiquitous defense monitoring system based on the Internet of Things (IoT) that is designed to provide security for individuals. The system uses sensors like a Global Positioning System (GPS), temperature sensor, heartbeat sensor, and Passive Infrared Sensor embedded in a wearable device. When the sensors detect an abnormal heartbeat rate or increase in temperature, indicating distress, the system automatically sends alerts with the person's location to registered contacts and nearby police stations using IoT technologies like SIM800A modules and WiFi. The system is implemented using an Arduino Uno microcontroller, embedded C programming, and the Proteus simulation software. It aims to help individuals in dangerous situations by quickly notifying authorities and
IRJET- Threat Detection in Hostile Environment with Deep Learning based on Dr...
This document proposes a threat detection system using a camera-equipped drone and deep learning. The system has two main modules: 1) A threat detector module that uses the YOLO object detection algorithm to detect threats in video frames captured by the drone camera. 2) A threat identifier module that uses a convolutional neural network to identify the specific type of any threats detected. The system is designed to run on computing hardware on the drone and report detections to a ground control station. Experimental results show the threat detector module can accurately detect threats in test images not used for training.
BE-EEE-8th sem-Presentation Artificial intelligence in security managenent
Seminar on “An Overview on Use of Artificial Intelligence Techniques in Effective Security Management System”
Home Security Systems using Camera and Android Application
This document describes a home security system that uses a camera and Android application. The system has two main modules: 1) a camera installed on the home's front door peephole that captures images of people at the door and 2) an Android app. When the camera detects motion, it takes a photo and compares it to stored images of authorized people. If it matches, the door opens. Otherwise, the photo is sent to the app for the user to decide whether to open the door remotely. The system is designed to be low-cost and provide security when users are away from home through image recognition and remote access via a mobile app.
Poster_PamelaDempster_40096050
This document discusses using a SIEM (security information and event management) architecture to detect and mitigate brute force attacks. It describes creating a virtual test environment with a Windows server as a victim, a Kali Linux machine as an attacker, and a pfSense firewall. The goal is to determine if a SIEM can detect a brute force dictionary attack before sensitive data is stolen or damage occurs. The results found that when the attack was run slowly, it was not detected, but filtering rules in the SIEM were able to detect a brute force HTTP attack. Therefore, SIEMs can help detect and mitigate brute force attacks if properly configured.
Embedded artificial intelligence system using deep learning and raspberrypi f...
Melanoma is a kind of skin cancer that originates in melanocytes responsible for producing melanin, it can be a severe and potentially deadly form of cancer because it can metastasize to other regions of the body if not detected and treated early. To facilitate this process, Recently, various computer-assisted low-cost, reliable, and accurate diagnostic systems have been proposed based on artificial intelligence (AI) algorithms, particularly deep learning techniques. This work proposed an innovative and intelligent system that combines the internet of things (IoT) with a Raspberry Pi connected to a camera and a deep learning model based on the deep convolutional neural network (CNN) algorithm for real-time detection and classification of melanoma cancer lesions. The key stages of our model before serializing to the Raspberry Pi: Firstly, the preprocessing part contains data cleaning, data transformation (normalization), and data augmentation to reduce overfitting when training. Then, the deep CNN algorithm is used to extract the features part. Finally, the classification part with applied Sigmoid Activation Function. The experimental results indicate the efficiency of our proposed classification system as we achieved an accuracy rate of 92%, a precision of 91%, a sensitivity of 91%, and an area under the curve- receiver operating characteristics (AUC-ROC) of 0.9133.
Similar to Poster: Monash Research Month 2009 (20)
ATM Security System Based on the Video Surveillance Using Neural Networks
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Weapon detection using artificial intelligence and deep learning for security...
Weapon detection using artificial intelligence and deep learning for security...
IRJET - Real-Time Analysis of Video Surveillance using Machine Learning a...
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Motion Detection System for Security Using IoT- Survey
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IRJET- Smart and Ubiquitous Defense Monitoring System based on IoT
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Artificial_intelligence_in_cctv_survelliance FINAL.pptx
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IRJET- Threat Detection in Hostile Environment with Deep Learning based on Dr...
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BE-EEE-8th sem-Presentation Artificial intelligence in security managenent
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More from Mahfuzul Haque
Dependency inversion using ports and adapters
This document discusses the Ports and Adapters architecture for decoupling an application's core business logic from external dependencies and allowing different services to be plugged in. The aims are to decouple the core logic, allow different services to be plugged in and removed easily, and make the application framework agnostic. An example order processing application is used to show how it evolves from being tightly coupled to external dependencies to following the Ports and Adapters pattern using interfaces, ports, and adapters to isolate the core logic. Code examples are provided in a GitHub repository linked in the document.
Resilient machine learning systems for health analytics
Tech companies are shifting more towards machine learning or AI-first strategy. What does that mean to them? What does that mean to us? Growing number of aging population and shrinking funding are putting enormous pressure to the overall healthcare system for keeping up with the desired quality of care with limited resources. Thus, there is an increasing focus on machine learning capabilities for just-in-time alerts to predict various future events so that undesirable incidents can be reduced by giving attention to the right people at the right time. This talk explores the underlying framework behind such capabilities, various strategies for a resilient system, and the role of a machine learning platform.
Talk 2012-icmew-event
This document proposes an approach for abnormal event detection in unseen scenarios using blob-level statistical analysis of video frames. The approach extracts frame-level features from foreground blobs using measures like blob area, count, and distance. These are transformed into temporal features considering speed and order. Event models are built offline using these features and an SVM classifier. During operation, only selected frame and temporal features are extracted in real-time, without additional training or tuning. Experiments on public safety and crowd datasets show the approach can detect events in unseen environments, outperforming optical flow-based methods.
Talk 2011-buet-perception-event
This document discusses a new technique called Perceptual Mixture of Gaussians (PMOG) for background subtraction in video analytics. PMOG incorporates characteristics of human visual perception to improve detection quality. It uses a realistic background value prediction based on the most recent observation. Detection threshold is set based on Weber's law and a just-noticeable difference threshold. Frame-level features are extracted from foreground blobs and transformed into temporal features for event detection. Experiments show PMOG has higher stability across environments and superior detection quality compared to other techniques. It provides an effective approach for abnormal event detection without requiring context information.
Talk 2010-monash-seminar-panic-driven-event-detection
This document presents a method for panic-driven event detection from surveillance video without using track and motion features. The method extracts frame-level features from each video frame independently, then transforms those into temporal features considering speed and order to characterize events. It was tested on four events from the CAVIAR dataset with over 80% accuracy and shown to detect abnormal events in three sites from the UMN dataset. The approach aims to develop a more generic event detection framework compared to tracking or motion-based methods.
Talk 2009-monash-seminar-perception
This document proposes a new object detection technique based on human visual perception. The technique uses a pixel-based mixture of Gaussians (MOG) model for environment modelling. It incorporates two human perceptual characteristics: a model reference point instead of the Gaussian mean, and a model extent based on a human perceivable intensity threshold rather than the Gaussian variance. Experiments on 50 test sequences show the technique achieves higher stability across learning rates and better detection quality at fast learning rates compared to existing techniques. Future work will explore applying this approach to multimodal scenarios.
Talk 2009-monash-seminar-intelligent-video-surveillance
This document describes a behaviour recognition framework for intelligent visual surveillance. It discusses the need for intelligent surveillance due to the large number of cameras now deployed. The framework aims to aid human monitors by automatically detecting abnormal group behaviors using computer vision and machine learning techniques. It extracts both frame-level and temporal features from video and classifies behaviors like mob violence, crowding, and panic behaviors using individual SVM classifiers. The framework includes components for background modeling, feature extraction at both frame and temporal levels, and behavior classification. It was implemented in a system called VSTK and evaluated on benchmark datasets.
Talk 2009-monash-open-day-surveillance
The document discusses the increasing use of video surveillance cameras for security purposes. While cameras are common in many public and private locations, it is impossible for human operators to constantly monitor the many video streams from thousands of cameras. Therefore, new technologies use computer vision and machine learning techniques to automatically detect events and alert security. Researchers are developing new surveillance applications that can analyze video to separate moving objects, detect undesirable events, and notify security. These technologies aim to provide security monitoring with minimum human involvement.
Talk 2007-monash-seminar-behavior-recognition-framework
The document proposes a real-time behavior recognition framework for visual surveillance. It aims to recognize unusual, unsafe, and abnormal human and group behaviors from surveillance video in real-time. This would help human monitors and improve surveillance system reliability. The framework includes environment modeling to identify active regions, feature extraction and agent classification, agent tracking handling occlusion, and behavior recognition by learning and detecting patterns. Literature on these components is also reviewed, including using Gaussian mixture models for environment modeling and pixel-based approaches.
Kb hmm
Hidden Markov models (HMMs) are statistical models used to predict hidden or latent states from observable states. HMMs assume the system being modeled is a Markov process with unknown parameters. The challenge is to determine the hidden parameters from observable parameters without directly observing the hidden states. HMMs have transition probabilities between hidden states and emission probabilities of observable states from hidden states. Algorithms like Baum-Welch, Viterbi, and forward-backward are used to estimate HMM parameters from data and predict hidden states.
Kb gait-recognition
1. The document discusses gait recognition from video for biometric identification. It provides background on biometric recognition and discusses gait as an identifying biometric trait that can be captured from a distance.
2. Various research approaches to gait recognition are covered, including model-based, motion-based, and mixed approaches. Commonly used gait recognition databases are also listed.
3. Recent works applying techniques like matrix representations, Bayesian frameworks, and symmetry-based detection are summarized, demonstrating applications in human identification, activity recognition, and scene registration. Future directions discussed include improving performance under more natural conditions.
Kb behaviour-recognition
The document discusses different approaches to abnormal behavior recognition: supervised, unsupervised, and semi-supervised. The supervised approach relies on clearly labeled normal and abnormal data but abnormal behavior is rare and undefined. Unsupervised approaches either cluster patterns or build a database of normal behavior patterns to detect abnormalities. Semi-supervised uses labeled normal data to build a normal model and then learns the abnormal model unsupervised. All approaches still have issues like insufficient labeled data or inconsistent manual labeling.
Talk 2012-icmew-perception
The document proposes a perceptual mixture-of-Gaussians approach to background subtraction that adapts dynamically based on principles of human visual perception. It models the relationship between detectable foreground pixels and background pixels based on Weber's law of just-noticeable differences. This results in piecewise linear relationships and different adaptation speeds for scotopic and photopic vision. An evaluation of 50 test sequences shows the proposed approach achieves more realistic background value prediction, higher stability across scenarios, and superior detection quality compared to other statistical methods.
Poster: Monash Research Month 2007
This document proposes a robust moving object detection method for visual surveillance. It uses a Gaussian mixture model (GMM) that models each pixel as a mixture of Gaussian distributions to address multi-modal distributions from illumination variations or repetitive background motions. The technique analyzes input video frames to build GMM pixel models over time. Experimental results on test sequences show the proposed method has fewer false classifications than existing GMM methods, indicating it performs better moving object detection.
Poster: ICPR 2008
This paper presents a technique to improve Gaussian mixture models for robust object detection by modifying the new model induction logic and using intensity difference thresholding to detect objects from one or more background models. The proposed method eliminates drawbacks of poor Gaussian mixture quality, susceptibility to background/foreground data proportion, and instability with varying operating environments. Quantitative and qualitative evaluations on test video sequences show the proposed technique achieves lower error rates and better visual results compared to existing methods.
Poster: ICME 2010
This document presents an approach for panic-driven event detection from surveillance video streams without using track and motion features. It extracts frame-level and temporal features from foreground blobs detected through background subtraction. These features are used to train support vector machine models for different events. The approach is evaluated on unseen scenarios from a public dataset and shows promising detection results without additional training, outperforming an approach using only optical flow. Experimental results demonstrate the detection of events like meetings, runaways, and fights in different environments.
Poster: EII Winter School 2007
1. The document proposes a real-time background estimation method for video surveillance using a modified Gaussian Mixture Model (GMM) that estimates the background frame from input video frames rather than classifying pixels as foreground or background.
2. The proposed method models each pixel as a mixture of K Gaussian distributions learned over time and estimates the background, which is then subtracted from the current frame to extract the foreground.
3. Experimental results on real and synthesized videos show the proposed background estimation method performs better foreground extraction in dynamic scenes compared to existing GMM-based methods.
Poster: AVSS 2012
1) The document proposes a background subtraction technique called multi-hypothesis mixture-of-Gaussians (MH-MOG) that generates multiple detection hypotheses from a single background model to achieve superior foreground detection quality for real-time video analytics.
2) MH-MOG maintains a single background pixel model using a perception-aware mixture-of-Gaussians and generates two independent detection hypotheses: a perception-inspired hypothesis and a probabilistic hypothesis.
3) It computes confidence levels for each hypothesis and uses this information in a detection algorithm to produce a high quality foreground mask by maximizing the complementary strengths of the two hypotheses, outperforming conventional techniques that use multiple background models.
Poster: MMSP 2008
This document presents a hybrid object detection technique that combines adaptive background modeling using Gaussian mixture models with basic background subtraction. It constructs a pixel model for each pixel using a mixture of Gaussian distributions to model multi-modal distributions from moving foregrounds and repetitive background motions. The technique reduces noise, shadows, and trailing effects while maintaining stability across varying environments. Evaluation on 14 test sequences shows the technique achieves lower error rates than other methods with less variation across learning rates.
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Power Grid Model
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This video focuses on integration of Salesforce with Bonterra Impact Management.
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Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
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Main news related to the CCS TSI 2023 (2023/1695)
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The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
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How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
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- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
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DeFi represents a paradigm shift in the financial industry. Instead of relying on traditional, centralized institutions like banks, DeFi leverages blockchain technology to create a decentralized network of financial services. This means that financial transactions can occur directly between parties, without intermediaries, using smart contracts on platforms like Ethereum.
In 2024, we are witnessing an explosion of new DeFi projects and protocols, each pushing the boundaries of what’s possible in finance.
In summary, DeFi in 2024 is not just a trend; it’s a revolution that democratizes finance, enhances security and transparency, and fosters continuous innovation. As we proceed through this presentation, we'll explore the various components and services of DeFi in detail, shedding light on how they are transforming the financial landscape.
At Intelisync, we specialize in providing comprehensive DeFi development services tailored to meet the unique needs of our clients. From smart contract development to dApp creation and security audits, we ensure that your DeFi project is built with innovation, security, and scalability in mind. Trust Intelisync to guide you through the intricate landscape of decentralized finance and unlock the full potential of blockchain technology.
Ready to take your DeFi project to the next level? Partner with Intelisync for expert DeFi development services today!
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
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7. What is Prometheus?
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9. What is Camel K?
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10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
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12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
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3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
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