This document discusses how video analytics can provide operational, marketing, and security insights by applying computer vision algorithms to existing CCTV video feeds. Specifically:
- Video analytics technologies can automate the analysis of large volumes of video data that would otherwise overwhelm human operators, and provide insights beyond just security like optimizing operations and understanding customer behavior.
- Advancements in video analytics technologies now enable capabilities like people counting, queue monitoring, and detecting pre-defined scenarios to generate actionable insights.
- As video analytics technologies continue advancing and becoming more affordable, organizations are using them to gain real-time insights into space usage, foot traffic, wait times, and typical usage patterns to optimize operations and target marketing.
A brief Introduction on Video surveillance TechnologyAneesh Suresh
Video surveillance technology and city-wide surveillance systems in Mumbai could provide several benefits. Such a system could monitor activity across the city in real-time for security purposes like detecting suspicious vehicles or criminal behavior. It would require hundreds of cameras, networking infrastructure, and analytics software. A surveillance system would help authorities with law enforcement, disaster response, and infrastructure monitoring while providing increased safety and traffic information for citizens. However, continuous video monitoring raises privacy issues that would need to be addressed.
The document describes a project that aims to develop a mobile application for real-time object and pose detection. The application will take in a real-time image as input and output bounding boxes identifying the objects in the image along with their class. The methodology involves preprocessing the image, then using the YOLO framework for object classification and localization. The goals are to achieve high accuracy detection that can be used for applications like vehicle counting and human activity recognition.
Face recognition using artificial neural networkSumeet Kakani
This document provides an overview of a face recognition system that uses artificial neural networks. It describes the structure and processing of artificial neural networks, including convolutional networks. It discusses how the system works, including local image sampling, the self-organizing map, and the convolutional network. It then provides details about the implementation and applications of the system for face recognition, and concludes by discussing the benefits of the system.
This presentation about Scikit-learn will help you understand what is Scikit-learn, what can we achieve using Scikit-learn and a demo on how to use Scikit-learn in Python. Scikit is a powerful and modern machine learning python library. It's a great tool for fully and semi-automated advanced data analysis and information extraction. There are a lot of reasons why Scikit-Learn is a preferred machine learning tool. It has efficient tools to identify and organize problems, such as whether it fits a supervised or unsupervised learning model. It contains many free and open data sets. It has a rich set of built-in libraries for learning and predicting. It provides model support for every problem type. It also has built-in functions such as pickle for model persistence. It is supported by a huge open source community and vendor base. Now, let us get started and understand Sciki-Learn in detail.
Below topics are explained in this Scikit-Learn presentation:
1. What is Scikit-learn?
2. What we can achieve using Scikit-learn
3. Demo
Simplilearn’s Python Training Course is an all-inclusive program that will introduce you to the Python development language and expose you to the essentials of object-oriented programming, web development with Django and game development. Python has surpassed Java as the top language used to introduce U.S. students to programming and computer science. This course will give you hands-on development experience and prepare you for a career as a professional Python programmer.
What is this course about?
The All-in-One Python course enables you to become a professional Python programmer. Any aspiring programmer can learn Python from the basics and go on to master web development & game development in Python. Gain hands-on experience creating a flappy bird game clone & website functionalities in Python.
What are the course objectives?
By the end of this online Python training course, you will be able to:
1. Internalize the concepts & constructs of Python
2. Learn to create your own Python programs
3. Master Python Django & advanced web development in Python
4. Master PyGame & game development in Python
5. Create a flappy bird game clone
The Python training course is recommended for:
1. Any aspiring programmer can take up this bundle to master Python
2. Any aspiring web developer or game developer can take up this bundle to meet their training needs
Learn more at https://www.simplilearn.com/mobile-and-software-development/python-development-training
This document presents a thesis on using YOLO v5 for real-time object detection of potholes, speed breakers, and vehicles. It discusses the objectives, methodology, and implementation of training a YOLO v5 model. The methodology section outlines the steps for preparing the dataset, environment setup, model training, inference on test images, and result visualization. The results section shows various performance metrics and detected objects on test images. It concludes the proposed method provides a preliminary solution for road object detection to help road maintenance agencies and drivers.
Short overview of Neural Networks Basics from our Software Developer Anatolii Shkurpylo
+ What is Neural Networks?
+ How NN works?
+ Simple NN in Python
A brief Introduction on Video surveillance TechnologyAneesh Suresh
Video surveillance technology and city-wide surveillance systems in Mumbai could provide several benefits. Such a system could monitor activity across the city in real-time for security purposes like detecting suspicious vehicles or criminal behavior. It would require hundreds of cameras, networking infrastructure, and analytics software. A surveillance system would help authorities with law enforcement, disaster response, and infrastructure monitoring while providing increased safety and traffic information for citizens. However, continuous video monitoring raises privacy issues that would need to be addressed.
The document describes a project that aims to develop a mobile application for real-time object and pose detection. The application will take in a real-time image as input and output bounding boxes identifying the objects in the image along with their class. The methodology involves preprocessing the image, then using the YOLO framework for object classification and localization. The goals are to achieve high accuracy detection that can be used for applications like vehicle counting and human activity recognition.
Face recognition using artificial neural networkSumeet Kakani
This document provides an overview of a face recognition system that uses artificial neural networks. It describes the structure and processing of artificial neural networks, including convolutional networks. It discusses how the system works, including local image sampling, the self-organizing map, and the convolutional network. It then provides details about the implementation and applications of the system for face recognition, and concludes by discussing the benefits of the system.
This presentation about Scikit-learn will help you understand what is Scikit-learn, what can we achieve using Scikit-learn and a demo on how to use Scikit-learn in Python. Scikit is a powerful and modern machine learning python library. It's a great tool for fully and semi-automated advanced data analysis and information extraction. There are a lot of reasons why Scikit-Learn is a preferred machine learning tool. It has efficient tools to identify and organize problems, such as whether it fits a supervised or unsupervised learning model. It contains many free and open data sets. It has a rich set of built-in libraries for learning and predicting. It provides model support for every problem type. It also has built-in functions such as pickle for model persistence. It is supported by a huge open source community and vendor base. Now, let us get started and understand Sciki-Learn in detail.
Below topics are explained in this Scikit-Learn presentation:
1. What is Scikit-learn?
2. What we can achieve using Scikit-learn
3. Demo
Simplilearn’s Python Training Course is an all-inclusive program that will introduce you to the Python development language and expose you to the essentials of object-oriented programming, web development with Django and game development. Python has surpassed Java as the top language used to introduce U.S. students to programming and computer science. This course will give you hands-on development experience and prepare you for a career as a professional Python programmer.
What is this course about?
The All-in-One Python course enables you to become a professional Python programmer. Any aspiring programmer can learn Python from the basics and go on to master web development & game development in Python. Gain hands-on experience creating a flappy bird game clone & website functionalities in Python.
What are the course objectives?
By the end of this online Python training course, you will be able to:
1. Internalize the concepts & constructs of Python
2. Learn to create your own Python programs
3. Master Python Django & advanced web development in Python
4. Master PyGame & game development in Python
5. Create a flappy bird game clone
The Python training course is recommended for:
1. Any aspiring programmer can take up this bundle to master Python
2. Any aspiring web developer or game developer can take up this bundle to meet their training needs
Learn more at https://www.simplilearn.com/mobile-and-software-development/python-development-training
This document presents a thesis on using YOLO v5 for real-time object detection of potholes, speed breakers, and vehicles. It discusses the objectives, methodology, and implementation of training a YOLO v5 model. The methodology section outlines the steps for preparing the dataset, environment setup, model training, inference on test images, and result visualization. The results section shows various performance metrics and detected objects on test images. It concludes the proposed method provides a preliminary solution for road object detection to help road maintenance agencies and drivers.
Short overview of Neural Networks Basics from our Software Developer Anatolii Shkurpylo
+ What is Neural Networks?
+ How NN works?
+ Simple NN in Python
Anomaly detection (or Outlier analysis) is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset. It is used is applications such as intrusion detection, fraud detection, fault detection and monitoring processes in various domains including energy, healthcare and finance.
In this workshop, we will discuss the core techniques in anomaly detection and discuss advances in Deep Learning in this field.
Through case studies, we will discuss how anomaly detection techniques could be applied to various business problems. We will also demonstrate examples using R, Python, Keras and Tensorflow applications to help reinforce concepts in anomaly detection and best practices in analyzing and reviewing results.
What you will learn:
Anomaly Detection: An introduction
Graphical and Exploratory analysis techniques
Statistical techniques in Anomaly Detection
Machine learning methods for Outlier analysis
Evaluating performance in Anomaly detection techniques
Detecting anomalies in time series data
Case study 1: Anomalies in Freddie Mac mortgage data
Case study 2: Auto-encoder based Anomaly Detection for Credit risk with Keras and Tensorflow
YouTube Link: https://youtu.be/vpOLiDyhNUA
** Machine Learning Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
This Edureka PPT on 'What is a Neural Network' will help you understand how Neural Networks can be used to solve complex, data-driven problems along with their real-world applications.
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. Well-researched domains of object detection include face detection and pedestrian detection. Object detection has applications in many areas of computer vision, including image retrieval and video surveillance.
Introduction to Computer Vision using OpenCVDylan Seychell
This is an introductory deck to computer vision using OpenCV and Python, through examples. This presentation is a step by step codelab through the basic functions of OpenCV.
OpenCV is an open-source library for computer vision and machine learning. The document discusses OpenCV's features including its modular structure, common computer vision algorithms like Canny edge detection, Hough transform, and cascade classifiers. Code examples are provided to demonstrate how to implement these algorithms using OpenCV functions and data types.
The document discusses the COCO dataset which contains 2.5 million labeled instances in 328,000 images across 91 object categories. It aims to address challenges in detecting non-iconic views of objects, contextual reasoning between objects, and precise 2D localization of objects. The dataset was collected from images featuring objects for ages 4-8 that received over 5,000 votes each. Images underwent category labeling, instance spotting, and instance segmentation annotation processes involving over 70,000 hours of work. Statistics on the dataset are provided and compared to other datasets. The document concludes by outlining plans to add annotations for stuff and human keypoints while removing some images.
Anomaly detection (Unsupervised Learning) in Machine LearningKuppusamy P
Anomaly detection techniques are used to identify rare items, events or observations which raise suspicions by differing significantly from the majority of the data. There are various types of anomalies including point anomalies, contextual anomalies and collective anomalies. Anomaly detection algorithms typically build a model of normal behavior and then label new data as normal or anomalous based on how well it fits the model. Common techniques include clustering, statistical methods and distance-based approaches. Applications include fraud detection, system failure diagnosis and cybersecurity.
Supervised and Unsupervised Learning In Machine Learning | Machine Learning T...Simplilearn
This document provides an overview of machine learning, including:
- Machine learning allows computers to learn from data without being explicitly programmed, through processes like analyzing data, training models on past data, and making predictions.
- The main types of machine learning are supervised learning, which uses labeled training data to predict outputs, and unsupervised learning, which finds patterns in unlabeled data.
- Common supervised learning tasks include classification (like spam filtering) and regression (like weather prediction). Unsupervised learning includes clustering, like customer segmentation, and association, like market basket analysis.
- Supervised and unsupervised learning are used in many areas like risk assessment, image classification, fraud detection, customer analytics, and more
This document discusses face recognition systems and the use of artificial neural networks for face recognition. It describes the basic steps in a face recognition system as face detection, alignment, feature extraction, and matching. Two types of neural networks that can be used for recognition are described - Radial Basis Function Networks and Back Propagation Networks. RBF Networks have an input, hidden, and output layer while BPN uses backpropagation of errors to adjust weights. The document also outlines some applications of face recognition systems such as ID verification and criminal investigations.
KNN Algorithm - How KNN Algorithm Works With Example | Data Science For Begin...Simplilearn
This K-Nearest Neighbor Classification Algorithm presentation (KNN Algorithm) will help you understand what is KNN, why do we need KNN, how do we choose the factor 'K', when do we use KNN, how does KNN algorithm work and you will also see a use case demo showing how to predict whether a person will have diabetes or not using KNN algorithm. KNN algorithm can be applied to both classification and regression problems. Apparently, within the Data Science industry, it's more widely used to solve classification problems. It’s a simple algorithm that stores all available cases and classifies any new cases by taking a majority vote of its k neighbors. Now lets deep dive into these slides to understand what is KNN algorithm and how does it actually works.
Below topics are explained in this K-Nearest Neighbor Classification Algorithm (KNN Algorithm) tutorial:
1. Why do we need KNN?
2. What is KNN?
3. How do we choose the factor 'K'?
4. When do we use KNN?
5. How does KNN algorithm work?
6. Use case - Predict whether a person will have diabetes or not
Simplilearn’s Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer
Why learn Machine Learning?
Machine Learning is rapidly being deployed in all kinds of industries, creating a huge demand for skilled professionals. The Machine Learning market size is expected to grow from USD 1.03 billion in 2016 to USD 8.81 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
You can gain in-depth knowledge of Machine Learning by taking our Machine Learning certification training course. With Simplilearn’s Machine Learning course, you will prepare for a career as a Machine Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
Learn more at: https://www.simplilearn.com
Neural style transfer allows computers to take an image and apply the visual style of another image to generate a new image. This is done by defining content and style cost functions that measure how well the generated image matches the content of the input image and the style of the style image. The algorithm then uses gradient descent to minimize the total cost and iteratively update the generated image until the content and style are closely matched.
Tracking is the problem of estimating the trajectory of an object as it moves around a scene. Motion tracking involves collecting data on human movement using sensors to control outputs like music or lighting based on performer actions. Motion tracking differs from motion capture in that it requires less equipment, is less expensive, and is concerned with qualities of motion rather than highly accurate data collection. Optical flow estimates the pixel-wise motion between frames in a video by calculating velocity vectors for each pixel.
This document summarizes a student project on face detection and recognition. The project used OpenCV with Python to detect faces in images and video in real-time. It extracts Haar features and compares them to a training database to recognize faces. The system was able to identify multiple faces with reasonable accuracy, though performance decreased with head tilts or low image quality. Future work could improve robustness to disguises and add emotion or gender analysis.
This document provides an agenda for a presentation on deep learning, neural networks, convolutional neural networks, and interesting applications. The presentation will include introductions to deep learning and how it differs from traditional machine learning by learning feature representations from data. It will cover the history of neural networks and breakthroughs that enabled training of deeper models. Convolutional neural network architectures will be overviewed, including convolutional, pooling, and dense layers. Applications like recommendation systems, natural language processing, and computer vision will also be discussed. There will be a question and answer section.
This Presentation is on the topic of Driver drowsiness Detection .
In this presentation We will discuss the Techniques used to detect drowsiness and compare some techniques
In the end we conclude and provide some suggestions regarding future work.
Thanks
It is a presentation for initial review of the project "Lane Detection". This project is useful for advanced driver assistance systems. We are developing this project by using computer vision. It includes gray scale conversion, noise reduction, canny edge detection, hough lane transform and some other user defined functions. The language we are using is python. Gray scale conversion converts the image from RGB format to gray. Since working with single colored channel image is much easier than working with three colored channel image. By using gaussian filter, noise reduction is performed. All the unwanted data, outliers, noisy data are removed. Simply the image is blurred. Next is canny edge detection, in this method edges present in the image are detected. And next region of interest is considered and hough lane transform is performed to get lanes on the road image.
This document provides instructions for installing OpenCV with Python on Windows and macOS systems. It also summarizes 9 example programs that demonstrate various computer vision techniques using OpenCV, such as reading and displaying images, opening video files and webcams, edge detection, line detection, color-based object tracking, contour classification, corner feature matching, and face recognition. The examples and more information are available at a provided GitHub URL. Reference materials for learning OpenCV with Python are also listed.
How Computer Vision is reshaping the finance and insurance industryAddepto
Learn about the competitive advantages that Computer Vision provides financial and insurance companies in smoothing customer experience and increasing general performance.
Table of contents:
1. What is Computer Vision?
2. How Computer Vision differs from Image Processing?
3. What are the benefits of Computer Vision technology?
4. How does it improve FinTech and Insuretech operations?
5. Real-life implementation of Computer Vision in the financial industry
6. Addepto Case Studies
Anomaly detection (or Outlier analysis) is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset. It is used is applications such as intrusion detection, fraud detection, fault detection and monitoring processes in various domains including energy, healthcare and finance.
In this workshop, we will discuss the core techniques in anomaly detection and discuss advances in Deep Learning in this field.
Through case studies, we will discuss how anomaly detection techniques could be applied to various business problems. We will also demonstrate examples using R, Python, Keras and Tensorflow applications to help reinforce concepts in anomaly detection and best practices in analyzing and reviewing results.
What you will learn:
Anomaly Detection: An introduction
Graphical and Exploratory analysis techniques
Statistical techniques in Anomaly Detection
Machine learning methods for Outlier analysis
Evaluating performance in Anomaly detection techniques
Detecting anomalies in time series data
Case study 1: Anomalies in Freddie Mac mortgage data
Case study 2: Auto-encoder based Anomaly Detection for Credit risk with Keras and Tensorflow
YouTube Link: https://youtu.be/vpOLiDyhNUA
** Machine Learning Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
This Edureka PPT on 'What is a Neural Network' will help you understand how Neural Networks can be used to solve complex, data-driven problems along with their real-world applications.
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. Well-researched domains of object detection include face detection and pedestrian detection. Object detection has applications in many areas of computer vision, including image retrieval and video surveillance.
Introduction to Computer Vision using OpenCVDylan Seychell
This is an introductory deck to computer vision using OpenCV and Python, through examples. This presentation is a step by step codelab through the basic functions of OpenCV.
OpenCV is an open-source library for computer vision and machine learning. The document discusses OpenCV's features including its modular structure, common computer vision algorithms like Canny edge detection, Hough transform, and cascade classifiers. Code examples are provided to demonstrate how to implement these algorithms using OpenCV functions and data types.
The document discusses the COCO dataset which contains 2.5 million labeled instances in 328,000 images across 91 object categories. It aims to address challenges in detecting non-iconic views of objects, contextual reasoning between objects, and precise 2D localization of objects. The dataset was collected from images featuring objects for ages 4-8 that received over 5,000 votes each. Images underwent category labeling, instance spotting, and instance segmentation annotation processes involving over 70,000 hours of work. Statistics on the dataset are provided and compared to other datasets. The document concludes by outlining plans to add annotations for stuff and human keypoints while removing some images.
Anomaly detection (Unsupervised Learning) in Machine LearningKuppusamy P
Anomaly detection techniques are used to identify rare items, events or observations which raise suspicions by differing significantly from the majority of the data. There are various types of anomalies including point anomalies, contextual anomalies and collective anomalies. Anomaly detection algorithms typically build a model of normal behavior and then label new data as normal or anomalous based on how well it fits the model. Common techniques include clustering, statistical methods and distance-based approaches. Applications include fraud detection, system failure diagnosis and cybersecurity.
Supervised and Unsupervised Learning In Machine Learning | Machine Learning T...Simplilearn
This document provides an overview of machine learning, including:
- Machine learning allows computers to learn from data without being explicitly programmed, through processes like analyzing data, training models on past data, and making predictions.
- The main types of machine learning are supervised learning, which uses labeled training data to predict outputs, and unsupervised learning, which finds patterns in unlabeled data.
- Common supervised learning tasks include classification (like spam filtering) and regression (like weather prediction). Unsupervised learning includes clustering, like customer segmentation, and association, like market basket analysis.
- Supervised and unsupervised learning are used in many areas like risk assessment, image classification, fraud detection, customer analytics, and more
This document discusses face recognition systems and the use of artificial neural networks for face recognition. It describes the basic steps in a face recognition system as face detection, alignment, feature extraction, and matching. Two types of neural networks that can be used for recognition are described - Radial Basis Function Networks and Back Propagation Networks. RBF Networks have an input, hidden, and output layer while BPN uses backpropagation of errors to adjust weights. The document also outlines some applications of face recognition systems such as ID verification and criminal investigations.
KNN Algorithm - How KNN Algorithm Works With Example | Data Science For Begin...Simplilearn
This K-Nearest Neighbor Classification Algorithm presentation (KNN Algorithm) will help you understand what is KNN, why do we need KNN, how do we choose the factor 'K', when do we use KNN, how does KNN algorithm work and you will also see a use case demo showing how to predict whether a person will have diabetes or not using KNN algorithm. KNN algorithm can be applied to both classification and regression problems. Apparently, within the Data Science industry, it's more widely used to solve classification problems. It’s a simple algorithm that stores all available cases and classifies any new cases by taking a majority vote of its k neighbors. Now lets deep dive into these slides to understand what is KNN algorithm and how does it actually works.
Below topics are explained in this K-Nearest Neighbor Classification Algorithm (KNN Algorithm) tutorial:
1. Why do we need KNN?
2. What is KNN?
3. How do we choose the factor 'K'?
4. When do we use KNN?
5. How does KNN algorithm work?
6. Use case - Predict whether a person will have diabetes or not
Simplilearn’s Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer
Why learn Machine Learning?
Machine Learning is rapidly being deployed in all kinds of industries, creating a huge demand for skilled professionals. The Machine Learning market size is expected to grow from USD 1.03 billion in 2016 to USD 8.81 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
You can gain in-depth knowledge of Machine Learning by taking our Machine Learning certification training course. With Simplilearn’s Machine Learning course, you will prepare for a career as a Machine Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
Learn more at: https://www.simplilearn.com
Neural style transfer allows computers to take an image and apply the visual style of another image to generate a new image. This is done by defining content and style cost functions that measure how well the generated image matches the content of the input image and the style of the style image. The algorithm then uses gradient descent to minimize the total cost and iteratively update the generated image until the content and style are closely matched.
Tracking is the problem of estimating the trajectory of an object as it moves around a scene. Motion tracking involves collecting data on human movement using sensors to control outputs like music or lighting based on performer actions. Motion tracking differs from motion capture in that it requires less equipment, is less expensive, and is concerned with qualities of motion rather than highly accurate data collection. Optical flow estimates the pixel-wise motion between frames in a video by calculating velocity vectors for each pixel.
This document summarizes a student project on face detection and recognition. The project used OpenCV with Python to detect faces in images and video in real-time. It extracts Haar features and compares them to a training database to recognize faces. The system was able to identify multiple faces with reasonable accuracy, though performance decreased with head tilts or low image quality. Future work could improve robustness to disguises and add emotion or gender analysis.
This document provides an agenda for a presentation on deep learning, neural networks, convolutional neural networks, and interesting applications. The presentation will include introductions to deep learning and how it differs from traditional machine learning by learning feature representations from data. It will cover the history of neural networks and breakthroughs that enabled training of deeper models. Convolutional neural network architectures will be overviewed, including convolutional, pooling, and dense layers. Applications like recommendation systems, natural language processing, and computer vision will also be discussed. There will be a question and answer section.
This Presentation is on the topic of Driver drowsiness Detection .
In this presentation We will discuss the Techniques used to detect drowsiness and compare some techniques
In the end we conclude and provide some suggestions regarding future work.
Thanks
It is a presentation for initial review of the project "Lane Detection". This project is useful for advanced driver assistance systems. We are developing this project by using computer vision. It includes gray scale conversion, noise reduction, canny edge detection, hough lane transform and some other user defined functions. The language we are using is python. Gray scale conversion converts the image from RGB format to gray. Since working with single colored channel image is much easier than working with three colored channel image. By using gaussian filter, noise reduction is performed. All the unwanted data, outliers, noisy data are removed. Simply the image is blurred. Next is canny edge detection, in this method edges present in the image are detected. And next region of interest is considered and hough lane transform is performed to get lanes on the road image.
This document provides instructions for installing OpenCV with Python on Windows and macOS systems. It also summarizes 9 example programs that demonstrate various computer vision techniques using OpenCV, such as reading and displaying images, opening video files and webcams, edge detection, line detection, color-based object tracking, contour classification, corner feature matching, and face recognition. The examples and more information are available at a provided GitHub URL. Reference materials for learning OpenCV with Python are also listed.
How Computer Vision is reshaping the finance and insurance industryAddepto
Learn about the competitive advantages that Computer Vision provides financial and insurance companies in smoothing customer experience and increasing general performance.
Table of contents:
1. What is Computer Vision?
2. How Computer Vision differs from Image Processing?
3. What are the benefits of Computer Vision technology?
4. How does it improve FinTech and Insuretech operations?
5. Real-life implementation of Computer Vision in the financial industry
6. Addepto Case Studies
CREATING CCTV CAMERA SYSTEM USING ARTIFICIAL INTELLIGENCE, IMAGE PROCESSING, ...IRJET Journal
This document describes a proposed project to create an improved CCTV camera system using artificial intelligence, image processing, and cybersecurity tools. The system aims to reduce vulnerabilities by implementing features like detecting suspicious gestures, recognizing vehicle license plates, detecting if people are wearing face masks, and securing the CCTV from cyber attacks. It will use techniques like deep learning, image processing, and cybersecurity methods. The system is intended to help improve security for businesses and reduce crime rates by automatically detecting criminal activities and traffic violations through advanced computer vision capabilities.
Smart City solution providers will face challenges in increasing network load due to the huge amounts of video data flowing through their networks. For cost-effective analytics, distributed architecture with user control is just the right solution required. In Smart Cities with varying applications of video analytics solutions in fields such as security systems, utilities operators, and emergency response systems, it gives users a simple way to pick the feed they would like, instrument the analysis they want, and report the way they require in a simple-configurable manner.
Video content analysis and video analytics are other terms for intelligent video. It analyses video surveillance feeds automatically and extracts vital data, such as the detection of an intruder in photographs. Intelligent Video is commonly used for video motion detection, video pattern matching, and auto-tracking. Surveillance cameras are increasingly being used by security organizations to keep a close eye on the surrounding environment 24 hours a day, seven days a week. IP technology enables the creation of an open, trustworthy, and scalable surveillance system. While the amount of video data available grows, a person can only see a limited amount of it. People are notorious for losing concentration quickly, and suspicious gestures on the screen are frequently ignored. Intelligent video monitors around the clock, seven days a week, and improves monitoring accuracy and efficacy. Here's another application for Intelligent Video. It transforms video data into a gold mine for business requirements. The camera captures customer behavior and provides critical data for marketing, retail operations, building layout design, traffic patterns, and other activities. Going through hours of video from a dozen cameras was a difficult, labor-intensive, and time-consuming operation. The intelligent video quickly analyses large amounts of video data. Intelligent video is undoubtedly useful for monitoring and a variety of corporate tasks, but it is costly and difficult to implement because it necessitates high-performance computers and specialized software.
Dr. Brian Lovell discusses emerging trends in face recognition technology that could help with security screening. He describes challenges with current automated border screening using facial verification and the need for more accurate face recognition of non-cooperative individuals in video streams. The document outlines Imagus Technology's approach to developing face recognition algorithms that can rapidly and reliably match low-quality images from surveillance cameras in real-time, which could help with challenges in border security and video surveillance more broadly.
Dr. Brian Lovell discusses emerging trends in video surveillance and face recognition technology. He describes how Imagus has developed a non-cooperative face recognition technology that can identify individuals from video in real-time, even when images are of low quality or the individuals are not cooperating. This technology has applications in airport security, access control, social media monitoring, and more. It leverages techniques like hardware acceleration and GPU processing to enable real-time matching of faces across hundreds of cameras.
Wearable Technology: Automotive's Next Digital FrontierCognizant
Wearables promise to impact the automotive value chain in a similar way to smartphones. But despite their great promise, wearables also lack proven use cases, requiring that companies proceed cautiously while ignoring wearables at their own peril.
The document discusses the security and surveillance marketplace for the SurveillancePoint service. It analyzes the current marketplace, trends, competitors, market size, and opportunities. SurveillancePoint is a global video surveillance, alarm, monitoring and tracking management system that can be accessed over the internet or private network from various devices. It integrates security equipment and provides remote access and monitoring capabilities, addressing a need in the market.
Internet of things report capgemini consulting are companies ready for the ...Rick Bouter
The document discusses how organizations are preparing for the opportunities and challenges presented by the Internet of Things (IoT). It finds that:
- Most organizations only offer basic IoT solutions and few provide advanced capabilities like remote operation or performance insights. Readiness varies by industry.
- Less than 30% of organizations generate revenue from IoT solutions and over 70% do not monetize these solutions.
- Most IoT solutions do not integrate with third-party products and services. Only 10-15% of organizations offer this functionality.
- While around 60% of organizations partner to develop IoT capabilities, few use acquisitions, platforms or APIs to build skills in this area.
-
The Internet of Things: Are Organizations Ready For A Multi-Trillion Dollar P...default default
The document discusses how organizations are preparing for the opportunities and challenges presented by the Internet of Things (IoT). It finds that:
- Most organizations only offer basic IoT solutions and few provide advanced capabilities like remote operation or performance insights. Readiness varies by industry.
- Less than 30% of organizations generate revenue from IoT solutions, though some pioneers like GE have found success. Emerging monetization models include tiered access to connectivity services.
- Integration with third parties is limited, with under 15% of solutions connecting to other products/services. While 60% use partnerships to develop IoT capabilities, few pursue acquisitions or open platforms.
- Organizations face challenges like
The Internet of Things: Are Organizations Ready For A Multi-Trillion Dollar P...Capgemini
The Internet is expanding. And this is not just in terms of getting accessible to more people; it is expanding beyond humans. Machines are becoming connected. Machines are talking to humans, but increasingly, they are also talking to one another. And this interconnectedness of machines, or the Internet of Things (IoT), is a potential multi-trillion dollar market that organizations can now tap into.
However, do organizations realize the scale of the opportunity? Capgemini Consulting conducted an extensive survey of IoT products and services of over 100 leading companies across North America and Europe. We also spoke at length with several industry executives at companies developing IoT solutions to understand the challenges companies face. This article presents the results of the survey and highlights the key hurdles companies are facing.
The document discusses mobile data capture and automation strategies. It provides examples of companies using mobile solutions to save time and reduce costs through mobile expense reporting and car dealership locator apps. The document also summarizes the state of the mobile market and capture technology, outlining key steps to mobile automation including image import, document analysis, optical character recognition and result processing. Case studies describe solutions from companies like Concur, Fujitsu, Kony and Neat that leverage mobile capture.
The document discusses mobile data capture and automation strategies. It provides examples of companies using mobile solutions to save time and reduce costs through mobile expense reporting and car dealership locator apps. The document also summarizes the state of the mobile market and capture technology, outlining key steps to mobile automation including image import, document analysis, optical character recognition and result processing. Case studies describe solutions from companies like Concur, Fujitsu, Kony and Neat that leverage mobile capture.
How technology is impacting the logistics industryNatalie Jones
Technology evolution is pushing the boundaries and changing the way business is done around the world. Advanced technology in the supply chain has improved productivity, minimizing expenses and failures.
1. Smart cards are credit card sized cards with embedded integrated chips that act as security tokens. They connect to readers through direct contact or wireless technologies like RFID.
2. Smart cards have various applications including use in telecommunications, identification, government, financial, healthcare, loyalty programs, and transportation.
3. Business intelligence refers to collecting, storing, and analyzing business data to inform management decisions. It includes tools like spreadsheets, reporting software, data visualization, data mining, and online analytical processing.
Reasons To Implement IoT In Transportation and LogisticsMobio Solutions
Are you curious how the Internet of Things (IoT) can revolutionize transportation and logistics? Check out this informative infographic that outlines the top reasons why implementing IoT technology can transform the industry. From optimizing routes and reducing fuel costs to enhancing fleet management and improving customer service, IoT has the potential to streamline operations and boost profitability. Don't miss out on this game-changing technology - look at the infographic now!
This document discusses several video surveillance trends to look out for in 2013:
1. Image quality will be a key battleground as manufacturers seek new technologies and camera features to improve image quality beyond just higher megapixel resolutions.
2. Mobile video streaming directly from surveillance cameras to control centers in real-time will become more common, allowing remote monitoring of incidents.
3. India will remain a high growth market for video surveillance equipment despite slowing economic growth, with double-digit market growth expected.
4. Suppliers will target smaller installations by keeping solutions simpler to expand their customer base.
Safety Check is an IoT solution to prevent increasing number of road accidents due to Drink driving, rash driving & fatigue.
It is a pocket-friendly solution that every responsible driver and car manufacturer would like to own in their cars.
Similar to Accenture video-analytics-operational-marketing-and-security-insights-from-cctv (20)
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
2. Organizations now have an
opportunity to increase the
returns they receive from their
existing video surveillance
infrastructures. By applying
sophisticated computer vision
algorithms to video feeds,
and adding an analytics layer
to existing video surveillance
systems, CCTV’s full potential
can begin to be realized.
1
Enhanced security capabilities
are just part of the picture.
For example, retailers and
customer-facing branch
network operators are using
insights from video analytics to
optimize their operations and
better understand customer
behaviors. Municipalities
and local governments
are increasing situational
awareness for intelligent cities.
And airport, train stations
and mass transit operators
are facilitating people flows,
detecting operational incidents,
and using predictive modeling
to optimize their operations.
The availability of advanced
video analytics technologies
means it is time to take video
surveillance to the next level—
in security, commercial and
public service environments.
3. Fuelled by concerns over terrorism and rising
crime rates, video surveillance installations
have surged in recent years. A study by
research group RNCOS estimates that the
global market for CCTV has been expanding
at a CAGR of 20.5 percent since 2012 and
will reach around US$23.5 billion by the end
of 20141
. While London has more cameras
deployed than any other major city, most
other industrialized countries have also
adopted this technology. Looking ahead,
China is predicted to become the world
leader in this field, accounting for 70 percent
of all video camera shipments by 20142
.
The rapid increase in CCTV installations
means that existing teams of operators
are unable to monitor the vast quantity of
video data that is being generated. In what
is a textbook example of data velocity4
overwhelming humans, estimates suggest that
up to 98 percent of CCTV footage remains
unseen5
. As a result, most video surveillance
installations are only used for forensic and
evidential purposes after the fact.
But with the introduction of video
analytics, most, if not all of that video data
can be more effectively scrutinized, leading
to major improvements in 24/7 video
surveillance capabilities. What is more, the
same technologies can also be used outside
the security domain to unlock exciting new
operations and marketing opportunities for
organizations across all industries.
“When thousands of cameras
run 24/7 in cities like Boston,
New York and Washington,
D.C., video surveillance
quickly becomes a big data
challenge. Analytics and
automation technologies are
the only answer3
.”
Realizing the potential with video analytics
Using video analytics, organizations no longer have to rely solely on human operators to process and analyze large volumes of
real-time or historical video data. Instead, they can tap into automatic surveillance and analysis of video streams (see Figure 1).
Figure 1: Potential of video analytics
From Video Surveillance
Operator monitoring 20+ screens
Showing feeds from 100+ cameras
Trying to stay alert!
Operations, marketing and security insights
To Operations Center
2
Video surveillance—a booming market
Video Management System
CCTV Cameras
Video Analytics
Video Management System
CCTV Cameras
4. There are a range of video analytics
technologies now available (see Figure
2), from basic motion detection to more
advanced capabilities such as counting,
tracking, anomaly detection and complex
behavioral analysis. By using advanced
operational analytics—a rule-based type
of data analytics that analyzes individual
video analytics events to detect pre-defined
scenarios—actionable insights can be
generated from the information received.
The benefits that this can have in the field
of security were thrown into stark relief
following the April 2013 Boston bombing,
when security agencies used video analytics
technologies to scrutinize the thousands
of hours of CCTV video footage that were
available6
. In another example, the UK and
Netherlands border agencies have deployed
an Accenture-provided self-service
passport control solution (incorporating
automated face capture and recognition)
to process millions of passengers each
year. Using self-service gates and
face recognition technologies to read
ePassports, validate documents, and carry
out face capture and matching, passengers
Figure 2: The art of the possible: A sample of the rich catalogue of video Analytics functionality
3
can cross the border in a self-service
fashion within, on average, eight seconds.
These are among the more well-publicized
examples of the benefits of video
analytics, and their successes are leading
organizations across all industries to invest
in these technologies. According to one
recent report7
, global “Intelligent Video
Surveillance (IVS) & Video Analytics (VA)”
industry revenues totaled US$13.5 billion
in 2012, and are estimated to reach US$39
billion in 2020.
People Recognition Crowd Counting
Post Event Analysis
Traffic Monitoring
Video Indexing
Object Recognition
Safety Alerts Cross-Camera Tracking Footfall Tracking
License Plate Recognition Incident Detection Left Object Detection
Augmented Reality Wide Spectrum Imagery Suspicious Behavior
5. Breakthrough advances
in technology
This investment is accelerating in step
with breakthrough advances in this area.
New technologies such as IP storage
are transforming how CCTV data can be
collected, stored and used. Cloud-based
video surveillance—“video surveillance
as a service” (VSaaS)—is set to take the
flexibility of CCTV installations to a new
level. And big data technologies can now
make unstructured CCTV data searchable
and available for analytic processes.
As CCTV goes digital, with major
improvements in optics, resolution and
frame rates, the quality of data is rising
all the time. Crucially, this data can now
be seamlessly integrated into the IT
infrastructure, where developments in
IP networks, bandwidth, processing and
storage mean that it can be stored and
combined) with other enterprise data to
enable insights that would previously have
been impossible.
With increasingly sophisticated algorithms
now available, the accuracy of these
insights is improving. New face recognition
algorithms in 2006 were 10 times more
accurate than those in 2002, and a hundred
times more accurate than those in 19958
.
People-counting algorithms have also been
maturing at pace. Analyzing the shape of a
living body (typically heads and shoulders)
through video pictures, these can now count
the number of people coming in or out of set
areas with accuracy above 95 percent9
.
Crucially too, analytics systems are not
just getting more “intelligent”. They are
also becoming more affordable, as the cost
of image processors and communications
systems continues to fall.
Extending the value
of video analytics
As video analytics technologies continue
to evolve and become more affordable,
organizations are beginning to investigate
how they can be used to deliver heightened
visibility into operational performance, as
well as what is happening in the external
environment. This has multiple benefits.
For example, by being able to count the
number of people within a facility and
measure dwell time and crowd density—
where people are congregating and for how
long—an organization will know where to
optimize customer flows and how to target
marketing resources to maximum effect.
And because operations centers are alerted
when crowd numbers approach pre-defined
health and safety thresholds, they know
when to start restricting entrance.
Key information on physical space usage,
footfall, wait and service times and typical
usage patterns is all within reach in a real-
time, continuous, 24x7 fashion—rather than
through occasional manual observation
surveys.
A recent Accenture pilot illustrates the
benefits that this delivers in the commercial
sphere. An Accenture core video analytics
solution, which includes counting and
queue monitoring capabilities, has recently
been tested in South Africa. It is providing
operational, marketing and security insights
from CCTV video feeds, monitoring people
and vehicle flows, queues and waiting
times in customer service environments.
In another example, Accenture’s face
recognition solution has been successfully
used in a large European shopping center to
scan up to 15,000 customers per day, and
generate notifications for known individuals
with less than 0.5 percent false alerts.
Under pressure to do more with less,
municipalities and local governments are
embracing video analytics solutions that
can optimize utilization of limited resources
while maintaining day-to-day city operations
and improving emergency handling.
The Singapore Safe City Pilot is a prime
example. Along with its industry partners,
the Singapore government is piloting the
integration of advanced analytics into
existing sensors and systems across the
city. The goal is to maximize situational
awareness, streamline operations and
enhance response capabilities across several
different government agencies10
.
Since April 2013, Accenture has deployed
its Video Analytics Service Platform in
Singapore to connect to existing and new
sensor infrastructures (including dozens
of CCTV cameras), apply computer vision
and predictive analytics to surveillance
video feeds to detect various events, and
generate business alerts for six different
government agencies.
4
6. Why Accenture?
Building on its strong credentials in
Biometrics and Facial Recognition
Technology (FRT), Accenture has developed
a layered, modular, vendor agnostic
solution for video analytics that enables
organizations to unlock greater value from
their existing CCTV investments.
Illustrated in Figure 3, the Accenture
Video Analytics offering applies computer
vision algorithms to video feeds, adding an
analytics layer to existing video surveillance
systems to detect events of interest. By
applying streaming analytics, the Accenture
solution turns these events into business
alerts that are displayed on an advanced
touch-wall user interface.
Developed by Accenture’s Emerging
Technology Innovation teams in Sophia
Antipolis, France and Bangalore, this
software solution is now delivering results
for security services, public service agencies
and commercial organizations worldwide.
5
Figure 3: Accenture Video Analytics: A layered, modular solution
Visualization
Core Analytics
Data Sources
Video AnalyticsVideo Analytics
Social Media
Listening
Sensor
Monitoring
Operational
Systems
Monitoring
Face Matching
CCTV Cameras People 3rd
Party Sensors
3rd
Party Operational
Systems
Traffic Monitoring
Anomaly Detection
License Plate
Matching
Tracking
People Counting
Crowd Detection
Flow Management
Vehicle Classification
Operations External Systems
Operational and Predictive Analytics
7. The digitalization of video surveillance data, combined with wider availability of advanced
video analytics technologies, means that more organizations are turning to these solutions
to create intelligent business operations.
From automated passport control gates to next-generation applications in safe cities,
video analytics is already in use and providing tangible security, business and customer
service benefits.
As well as applying video analytics to video stream data to obtain deeper, richer operational
insights, organizations are taking their situational awareness to the next level and
strategically using these technologies to “engage with customers and citizens in new ways.
6
Video analytics: Toward intelligent
business operations