Video Analytics Using Deep Learning PDF Download

Author: Debjyoti Paul
Publisher: Apress
ISBN: 9781484237922
Category : Computers
Languages : en
Pages : 0

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Book Description
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

Author: Vaibhav Verdhan
Publisher: Apress
ISBN: 9781484266151
Category : Computers
Languages : en
Pages : 308

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Book Description
Organizations spend huge resources in developing software that can perform the way a human does. Image classification, object detection and tracking, pose estimation, facial recognition, and sentiment estimation all play a major role in solving computer vision problems. This book will bring into focus these and other deep learning architectures and techniques to help you create solutions using Keras and the TensorFlow library. You'll also review mutliple neural network architectures, including LeNet, AlexNet, VGG, Inception, R-CNN, Fast R-CNN, Faster R-CNN, Mask R-CNN, YOLO, and SqueezeNet and see how they work alongside Python code via best practices, tips, tricks, shortcuts, and pitfalls. All code snippets will be broken down and discussed thoroughly so you can implement the same principles in your respective environments. Computer Vision Using Deep Learning offers a comprehensive yet succinct guide that stitches DL and CV together to automate operations, reduce human intervention, increase capability, and cut the costs. What You'll Learn Examine deep learning code and concepts to apply guiding principals to your own projects Classify and evaluate various architectures to better understand your options in various use cases Go behind the scenes of basic deep learning functions to find out how they work Who This Book Is For Professional practitioners working in the fields of software engineering and data science. A working knowledge of Python is strongly recommended. Students and innovators working on advanced degrees in areas related to computer vision and Deep Learning.

Author: El-Sayed M. El-Alfy
Publisher: CRC Press
ISBN: 1000851907
Category : Computers
Languages : en
Pages : 361

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Book Description
Video has rich information including meta-data, visual, audio, spatial and temporal data which can be analysed to extract a variety of low and high-level features to build predictive computational models using machine-learning algorithms to discover interesting patterns, concepts, relations, and associations. This book includes a review of essential topics and discussion of emerging methods and potential applications of video data mining and analytics. It integrates areas like intelligent systems, data mining and knowledge discovery, big data analytics, machine learning, neural network, and deep learning with focus on multimodality video analytics and recent advances in research/applications. Features: 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 indoors and outdoors applications of smart cameras in smart environments, smart homes, and smart cities. This book aims at researchers, professionals and graduate students in image processing, video analytics, computer science and engineering, signal processing, machine learning, and electrical engineering.

Author: Debarati Bhunia Chakraborty
Publisher: World Scientific
ISBN: 9811227136
Category : Computers
Languages : en
Pages : 256

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Book Description
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.

Author: Kathrin Melcher
Publisher: Packt Publishing Ltd
ISBN: 180056242X
Category : Computers
Languages : en
Pages : 385

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Book Description
Discover how to integrate KNIME Analytics Platform with deep learning libraries to implement artificial intelligence solutions Key FeaturesBecome well-versed with KNIME Analytics Platform to perform codeless deep learningDesign and build deep learning workflows quickly and more easily using the KNIME GUIDiscover different deployment options without using a single line of code with KNIME Analytics PlatformBook Description KNIME Analytics Platform is an open source software used to create and design data science workflows. This book is a comprehensive guide to the KNIME GUI and KNIME deep learning integration, helping you build neural network models without writing any code. It’ll guide you in building simple and complex neural networks through practical and creative solutions for solving real-world data problems. Starting with an introduction to KNIME Analytics Platform, you’ll get an overview of simple feed-forward networks for solving simple classification problems on relatively small datasets. You’ll then move on to build, train, test, and deploy more complex networks, such as autoencoders, recurrent neural networks (RNNs), long short-term memory (LSTM), and convolutional neural networks (CNNs). In each chapter, depending on the network and use case, you’ll learn how to prepare data, encode incoming data, and apply best practices. By the end of this book, you’ll have learned how to design a variety of different neural architectures and will be able to train, test, and deploy the final network. What you will learnUse various common nodes to transform your data into the right structure suitable for training a neural networkUnderstand neural network techniques such as loss functions, backpropagation, and hyperparametersPrepare and encode data appropriately to feed it into the networkBuild and train a classic feedforward networkDevelop and optimize an autoencoder network for outlier detectionImplement deep learning networks such as CNNs, RNNs, and LSTM with the help of practical examplesDeploy a trained deep learning network on real-world dataWho this book is for This book is for data analysts, data scientists, and deep learning developers who are not well-versed in Python but want to learn how to use KNIME GUI to build, train, test, and deploy neural networks with different architectures. The practical implementations shown in the book do not require coding or any knowledge of dedicated scripts, so you can easily implement your knowledge into practical applications. No prior experience of using KNIME is required to get started with this book.

Author: Nikhil Singh
Publisher: BPB Publications
ISBN: 9388511859
Category : Computers
Languages : en
Pages : 222

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Book Description
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

Author: Vimal Shanmuganathan
Publisher:
ISBN: 9788770226622
Category :
Languages : en
Pages :

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Book Description
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.

Author: Thomas, J. Joshua
Publisher: IGI Global
ISBN: 1799811948
Category : Computers
Languages : en
Pages : 355

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Book Description
Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there’s a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.

Author: Singh Nikhil
Publisher: BPB Publications
ISBN: 9388176618
Category : Computers
Languages : en
Pages : 227

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Book Description
Master Computer Vision concepts using Deep Learning with easy-to-follow steps Key Featuresa- Setting up the Python and TensorFlow environmenta- Learn core Tensorflow concepts with the latest TF version 2.0a- Learn Deep Learning for computer vision applications a- Understand different computer vision concepts and use-casesa- Understand different state-of-the-art CNN architectures a- Build deep neural networks with transfer Learning using features from pre-trained CNN modelsa- Apply computer vision concepts with easy-to-follow code in Jupyter NotebookDescriptionThis 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. 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.What will you learnThis 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. Who this book is forThis 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. Table of Contents1. Introduction to TensorFlow2. Introduction to Neural Networks 3. Convolutional Neural Network 4. CNN Architectures5. Sequential ModelsAbout the AuthorNikhil Singh is an accomplished data scientist and currently working as the Lead Data Scientist at Proarch IT Solutions Pvt. Ltd in London. He has experience in designing and delivering complex and innovative computer vision and NLP centred solutions for a large number of global companies. He has been an AI consultant to a few companies and mentored many apprentice Data Scientists. His LinkedIn Profile: https://www.linkedin.com/in/nikhil-singh-b953ba122/Paras Ahuja is a seasoned data science practitioner and currently working as the Lead Data Scientist at Reliance Jio in Hyderabad. He has good experience in designing and deploying deep learning-based Computer Vision and NLP-based solutions. He has experience in developing and implementing state-of-the-art automatic speech recognition systems.His LinkedIn Profile: https://www.linkedin.com/in/parasahuja

Author: Anand Vemula
Publisher: Anand Vemula
ISBN:
Category : Computers
Languages : en
Pages : 36

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Book Description
This book equips you with the skills to build and deploy custom vision AI applications for real-time video analysis. Whether you're a developer, researcher, or enthusiast, you'll gain a comprehensive understanding of NVIDIA's powerful toolkit, from training models to real-world deployment. Part 1: Introduction to Vision AI and Deep Learning Lays the groundwork for computer vision and deep learning concepts. Explains how these technologies are used in real-world applications. Introduces NVIDIA TAO and DeepStream, your one-stop shop for vision AI development. Part 2: NVIDIA TAO Toolkit - Your Vision AI Training Companion Guides you through setting up and navigating the user-friendly TAO interface. Explains how to prepare your data for efficient model training. Covers techniques for leveraging pre-trained models and adding new classes. Dives into model training optimization and explores methods for reducing model size for deployment. Teaches you how to export your trained models for seamless integration with DeepStream. Part 3: NVIDIA DeepStream SDK - Unleashing Your Vision AI in Real-Time Unveils the core functionalities and architecture of DeepStream for real-time video analytics. Explains how DeepStream leverages GStreamer, a powerful framework, for efficient data processing. Provides step-by-step guidance on building real-time video analytics pipelines using DeepStream. Explores various DeepStream plugins for common tasks like decoding, inference, and displaying results. Demonstrates how to integrate your TAO models into DeepStream pipelines for real-world applications. Part 4: Deployment and Optimization - Taking Your DeepStream Applications to the Real World Explores different deployment options for your DeepStream applications, from edge devices to cloud servers. Provides optimization techniques to ensure your applications run smoothly and efficiently. Covers methods for improving inference speed and resource utilization. Explains how to profile and debug your DeepStream pipelines for optimal performance. By combining the power of TAO for model training with DeepStream for real-time deployment, you'll be equipped to build cutting-edge vision AI applications that analyze and understand the visual world around you. Get started today and unlock the potential of real-time video analytics!