Video Analytics Using Deep Learning

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Video Analytics Using Deep Learning

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Author : Debjyoti Paul
language : en
Publisher: Apress
Release Date : 2020-01-13

Video Analytics Using Deep Learning written by Debjyoti Paul and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-13 with Computers categories.

Build analytics for video using TensorFlow, Keras, and YOLO. This book guides you through the field of deep learning starting with neural networks, taking a deep dive into convolutional neural networks, recurrent neural networks, and long short-term memory (LSTM) networks. Video Analytics Using Deep Learning closes with practical examples of building image filters and video masking using generative models. The examples within the book cover topics from domains such as traffic recognition for self-driving cars; face recognition and emotion analysis for retail analytics; object and tamper detection for safety and security; and image filters and video masking for social networks and web applications. To enable you to make a smooth transition into deep learning, the book covers mathematical pre-requisites and includes an introduction to deep learning. You’ll also cover topics such as storage of large video content for processing on the cloud and working with the connectors involved. All the code and samples in the book are provided as iPython. What You Will Learn Master TensorFlow, Keras, and YOLO Work with face recognition, age detection, and gender identification Apply CNN, RNN and generative models in deep learning Use emotion analysis and gesture detection Carry out traffic recognition in real-time Who This Book Is For Data scientists and machine learning developers looking to build applications based on video in finance, healthcare, automotive, transport, safety/security, and home automation. /div

Intelligent Image And Video Analytics

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Author : El-Sayed M. El-Alfy
language : en
Publisher: CRC Press
Release Date : 2023-04-12

Intelligent Image And Video Analytics written by El-Sayed M. El-Alfy and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-12 with Computers categories.

Provides up-to-date coverage of the state-of-the-art techniques in intelligent video analytics Explores important applications that require techniques from both artificial intelligence and computer vision Describes multimodality video analytics for different applications Examines issues related to multimodality data fusion and highlights research challenges Integrates various techniques from video processing, data mining and machine learning which has many emerging indoor and outdoor applications of smart cameras in smart environments, smart homes, and smart cities

Deep Learning For Video Analytics Using Digital Twin

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Author : Vimal Shanmuganathan
language : en
Publisher:
Release Date : 2022-01-31

Deep Learning For Video Analytics Using Digital Twin written by Vimal Shanmuganathan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-31 with categories.

This volume provides a forum for researchers, especially those with an interest in efficiency, to examine challenging research questions, showcase state-of-the-art, and share breakthroughs in Multimedia Data Handling using Digital Twin technology.

Granular Video Computing With Rough Sets Deep Learning And In Iot

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Author : Debarati Bhunia Chakraborty
language : en
Publisher: World Scientific
Release Date : 2021-02-04

Granular Video Computing With Rough Sets Deep Learning And In Iot written by Debarati Bhunia Chakraborty and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-04 with Computers categories.

This volume links the concept of granular computing using deep learning and the Internet of Things to object tracking for video analysis. It describes how uncertainties, involved in the task of video processing, could be handled in rough set theoretic granular computing frameworks. Issues such as object tracking from videos in constrained situations, occlusion/overlapping handling, measuring of the reliability of tracking methods, object recognition and linguistic interpretation in video scenes, and event prediction from videos, are the addressed in this volume. The book also looks at ways to reduce data dependency in the context of unsupervised (without manual interaction/ labeled data/ prior information) training.This book may be used both as a textbook and reference book for graduate students and researchers in computer science, electrical engineering, system science, data science, and information technology, and is recommended for both students and practitioners working in computer vision, machine learning, video analytics, image analytics, artificial intelligence, system design, rough set theory, granular computing, and soft computing.

Granular Video Computing

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Author : Debarati Bhunia Chakraborty
language : en
Publisher:
Release Date : 2021

Granular Video Computing written by Debarati Bhunia Chakraborty and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Automatic tracking categories.

"This volume links the concept of granular computing using deep learning and the Internet of Things to object tracking for video analysis. It describes how uncertainties, involved in the task of video processing, could be handled in rough set theoretic granular computing frameworks. Issues such as object tracking from videos in constrained situations, occlusion/overlapping handling, measuring of the reliability of tracking methods, object recognition and linguistic interpretation in video scenes, and event prediction from videos, are the addressed in this volume. The book also looks at ways to reduce data dependency in the context of unsupervised (without manual interaction/ labeled data/ prior information) training. This book may be used both as a textbook and reference book for graduate students and researchers in computer science, electrical engineering, system science, data science, and information technology, and is recommended for both students and practitioners working in computer vision, machine learning, video analytics, image analytics, artificial intelligence, system design, rough set theory, granular computing, and soft computing"--

Roadside Video Data Analysis

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Author : Brijesh Verma
language : en
Publisher: Springer
Release Date : 2017-04-28

Roadside Video Data Analysis written by Brijesh Verma and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-04-28 with Technology & Engineering categories.

This book highlights the methods and applications for roadside video data analysis, with a particular focus on the use of deep learning to solve roadside video data segmentation and classification problems. It describes system architectures and methodologies that are specifically built upon learning concepts for roadside video data processing, and offers a detailed analysis of the segmentation, feature extraction and classification processes. Lastly, it demonstrates the applications of roadside video data analysis including scene labelling, roadside vegetation classification and vegetation biomass estimation in fire risk assessment.

Hands On Convolutional Neural Networks With Tensorflow

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Author : Iffat Zafar
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-08-28

Hands On Convolutional Neural Networks With Tensorflow written by Iffat Zafar and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-28 with Computers categories.

Learn how to apply TensorFlow to a wide range of deep learning and Machine Learning problems with this practical guide on training CNNs for image classification, image recognition, object detection and many computer vision challenges. Key Features Learn the fundamentals of Convolutional Neural Networks Harness Python and Tensorflow to train CNNs Build scalable deep learning models that can process millions of items Book Description Convolutional Neural Networks (CNN) are one of the most popular architectures used in computer vision apps. This book is an introduction to CNNs through solving real-world problems in deep learning while teaching you their implementation in popular Python library - TensorFlow. By the end of the book, you will be training CNNs in no time! We start with an overview of popular machine learning and deep learning models, and then get you set up with a TensorFlow development environment. This environment is the basis for implementing and training deep learning models in later chapters. Then, you will use Convolutional Neural Networks to work on problems such as image classification, object detection, and semantic segmentation. After that, you will use transfer learning to see how these models can solve other deep learning problems. You will also get a taste of implementing generative models such as autoencoders and generative adversarial networks. Later on, you will see useful tips on machine learning best practices and troubleshooting. Finally, you will learn how to apply your models on large datasets of millions of images. What you will learn Train machine learning models with TensorFlow Create systems that can evolve and scale during their life cycle Use CNNs in image recognition and classification Use TensorFlow for building deep learning models Train popular deep learning models Fine-tune a neural network to improve the quality of results with transfer learning Build TensorFlow models that can scale to large datasets and systems Who this book is for This book is for Software Engineers, Data Scientists, or Machine Learning practitioners who want to use CNNs for solving real-world problems. Knowledge of basic machine learning concepts, linear algebra and Python will help.

Video Data Analytics For Smart City Applications Methods And Trends

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Author : Abhishek Singh Rathore
language : en
Publisher: Bentham Science Publishers
Release Date : 2023-04-20

Video Data Analytics For Smart City Applications Methods And Trends written by Abhishek Singh Rathore and has been published by Bentham Science Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-20 with Computers categories.

Video data analytics is rapidly evolving and transforming the way we live in urban environments. Video Data Analytics for Smart City Applications: Methods and Trends, data science experts present a comprehensive review of the latest advances and trends in video analytics technologies and their extensive applications in smart city planning and engineering. The book covers a wide range of topics including object recognition, action recognition, violence detection, and tracking, exploring deep learning approaches and other techniques for video data analytics. It also discusses the key enabling technologies for smart cities and homes and the scope and application of smart agriculture in smart cities. Moreover, the book addresses the challenges and security issues in terahertz band for wireless communication and the empirical impact of AI and IoT on performance management. One contribution also provides a review of the progress in achieving the Jal Jeevan Mission Goals for institutional capacity building in the Indian State of Chhattisgarh. For researchers, computer scientists, data analytics professionals, smart city planners and engineers, this book provides detailed references for further reading and demonstrates how technologies are serving their use-cases in the smart city. The book highlights the advances and trends in video analytics technologies and extensively addresses key themes, making it an essential resource for anyone looking to gain a comprehensive understanding of video data analytics for smart city applications.

Fundamentals Of Deep Learning And Computer Vision

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Author : Nikhil Singh
language : en
Publisher: BPB Publications
Release Date : 2020-02-24

Fundamentals Of Deep Learning And Computer Vision written by Nikhil Singh and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-24 with Computers categories.

Master Computer Vision concepts using Deep Learning with easy-to-follow steps DESCRIPTIONÊ This book starts with setting up a Python virtual environment with the deep learning framework TensorFlow and then introduces the fundamental concepts of TensorFlow. Before moving on to Computer Vision, you will learn about neural networks and related aspects such as loss functions, gradient descent optimization, activation functions and how backpropagation works for training multi-layer perceptrons. To understand how the Convolutional Neural Network (CNN) is used for computer vision problems, you need to learn about the basic convolution operation. You will learn how CNN is different from a multi-layer perceptron along with a thorough discussion on the different building blocks of the CNN architecture such as kernel size, stride, padding, and pooling and finally learn how to build a small CNN model.Ê Next, you will learn about different popular CNN architectures such as AlexNet, VGGNet, Inception, and ResNets along with different object detection algorithms such as RCNN, SSD, and YOLO. The book concludes with a chapter on sequential models where you will learn about RNN, GRU, and LSTMs and their architectures and understand their applications in machine translation, image/video captioning and video classification. KEY FEATURESÊ Setting up the Python and TensorFlow environment Learn core Tensorflow concepts with the latest TF version 2.0 Learn Deep Learning for computer vision applicationsÊ Understand different computer vision concepts and use-cases Understand different state-of-the-art CNN architecturesÊ Build deep neural networks with transfer Learning using features from pre-trained CNN models Apply computer vision concepts with easy-to-follow code in Jupyter Notebook WHAT WILL YOU LEARNÊ This book will help the readers to understand and apply the latest Deep Learning technologies to different interesting computer vision applications without any prior domain knowledge of image processing. Thus, helping the users to acquire new skills specific to Computer Vision and Deep Learning and build solutions to real-life problems such as Image Classification and Object Detection. This book will serve as a basic guide for all the beginners to master Deep Learning and Computer Vision with lucid and intuitive explanations using basic mathematical concepts. It also explores these concepts with popular the deep learning framework TensorFlow. WHO THIS BOOK IS FOR This book is for all the Data Science enthusiasts and practitioners who intend to learn and master Computer Vision concepts and their applications using Deep Learning. This book assumes a basic Python understanding with hands-on experience. A basic senior secondary level understanding of Mathematics will help the reader to make the best out of this book.Ê Table of Contents 1. Introduction to TensorFlow 2. Introduction to Neural NetworksÊ 3. Convolutional Neural NetworkÊÊ 4. CNN Architectures 5. Sequential Models

Deep Learning Applications

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Author : M. Arif Wani
language : en
Publisher: Springer Nature
Release Date : 2020-02-28

Deep Learning Applications written by M. Arif Wani and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-28 with Technology & Engineering categories.

This book presents a compilation of selected papers from the 17th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2018), focusing on use of deep learning technology in application like game playing, medical applications, video analytics, regression/classification, object detection/recognition and robotic control in industrial environments. It highlights novel ways of using deep neural networks to solve real-world problems, and also offers insights into deep learning architectures and algorithms, making it an essential reference guide for academic researchers, professionals, software engineers in industry, and innovative product developers.