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Author: Md. Atiqur Rahman Ahad
Publisher: Springer Science & Business Media
ISBN: 9491216201
Category : Computers
Languages : en
Pages : 228
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Book Description
Human action analyses and recognition are challenging problems due to large variations in human motion and appearance, camera viewpoint and environment settings. The field of action and activity representation and recognition is relatively old, yet not well-understood by the students and research community. Some important but common motion recognition problems are even now unsolved properly by the computer vision community. However, in the last decade, a number of good approaches are proposed and evaluated subsequently by many researchers. Among those methods, some methods get significant attention from many researchers in the computer vision field due to their better robustness and performance. This book will cover gap of information and materials on comprehensive outlook – through various strategies from the scratch to the state-of-the-art on computer vision regarding action recognition approaches. This book will target the students and researchers who have knowledge on image processing at a basic level and would like to explore more on this area and do research. The step by step methodologies will encourage one to move forward for a comprehensive knowledge on computer vision for recognizing various human actions.
Author: Md. Atiqur Rahman Ahad
Publisher: Springer Science & Business Media
ISBN: 9491216201
Category : Computers
Languages : en
Pages : 228
Get Book Here
Book Description
Human action analyses and recognition are challenging problems due to large variations in human motion and appearance, camera viewpoint and environment settings. The field of action and activity representation and recognition is relatively old, yet not well-understood by the students and research community. Some important but common motion recognition problems are even now unsolved properly by the computer vision community. However, in the last decade, a number of good approaches are proposed and evaluated subsequently by many researchers. Among those methods, some methods get significant attention from many researchers in the computer vision field due to their better robustness and performance. This book will cover gap of information and materials on comprehensive outlook – through various strategies from the scratch to the state-of-the-art on computer vision regarding action recognition approaches. This book will target the students and researchers who have knowledge on image processing at a basic level and would like to explore more on this area and do research. The step by step methodologies will encourage one to move forward for a comprehensive knowledge on computer vision for recognizing various human actions.
Author: Md. Atiqur Rahman Ahad
Publisher: Springer Science & Business Media
ISBN: 1447147308
Category : Computers
Languages : en
Pages : 132
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Book Description
Human action analysis and recognition is a relatively mature field, yet one which is often not well understood by students and researchers. The large number of possible variations in human motion and appearance, camera viewpoint, and environment, present considerable challenges. Some important and common problems remain unsolved by the computer vision community. However, many valuable approaches have been proposed over the past decade, including the motion history image (MHI) method. This method has received significant attention, as it offers greater robustness and performance than other techniques. This work presents a comprehensive review of these state-of-the-art approaches and their applications, with a particular focus on the MHI method and its variants.
Author: Mark Magic
Publisher:
ISBN: 9781086884470
Category :
Languages : en
Pages : 164
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Book Description
* Updated in August, 2019 with color printing! * Research fields: Computer Vision and Machine Learning. * Book Topic: Action recognition from videos. * Recognition Tool: Recurrent Neural Network (RNN) with LSTM (Long-Short Term Memory) layer and fully connected layer. * Programming Language: Step-by-step implementation with Python in Jupyter Notebook. * Major Steps: Building a network, training the network, testing the network, comparing the network with an SVM (Support Vector Machines) classifier. * Processing Units to Execute the Codes: CPU and GPU (on Google Colaboratory). * Image Feature Extraction Tool: Pretrained VGG16 network. * Dataset: UCF101 (the first 15 actions, 2010 videos). * Main Results: For the testing data, the highest prediction accuracy from the RNN is 86.97%, which is a little higher than that from the SVM classifier (86.09%). * Detailed Description: Recurrent Neural Network (RNN) is a great tool to do video action recognition. This book built an RNN with an LSTM (Long-Short Term Memory) layer and a fully connected layer to do video action recognition. The RNN was trained and evaluated with VGG16 Features that were saved in .mat files; the features were extracted from images with a modified pretrained VGG16 network; the images were converted from videos in the UCF101 dataset, which has 101 different actions including 13,320 videos; please notice that only the first 15 actions in this dataset were used to do the recognition. The codes were implemented step-by-step with Python in Jupyter Notebook, and they could be executed on both CPUs and GPUs; free GPUs on Google Colaboratory were used as hardware accelerator to do most of the calculations. For the purpose of getting a higher testing accuracy, the architecture of the network was regulated, and parameters of the network and its optimizer were fine-tuned. For comparison purpose only, an SVM (Support Vector Machines) classifier was trained and tested. For the first 15 actions in the UCF101 dataset, the highest prediction accuracy of the testing data from the RNN is 86.97%, which is a little higher than that from the SVM classifier (86.09%). In conclusion, the performances of the RNN and the SVM classifier are approximately the same for the task in this book, which is a little embarrassed. However, RNN does have its own advantages in many other cases in the fields of Computer Vision and Machine Learning, and the implementation in this book can be an introduction to this topic in order to throw out a minnow to catch a whale.
Author: Andrew Fitzgibbon
Publisher: Springer
ISBN: 3642337090
Category : Computers
Languages : en
Pages : 909
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Book Description
The seven-volume set comprising LNCS volumes 7572-7578 constitutes the refereed proceedings of the 12th European Conference on Computer Vision, ECCV 2012, held in Florence, Italy, in October 2012. The 408 revised papers presented were carefully reviewed and selected from 1437 submissions. The papers are organized in topical sections on geometry, 2D and 3D shapes, 3D reconstruction, visual recognition and classification, visual features and image matching, visual monitoring: action and activities, models, optimisation, learning, visual tracking and image registration, photometry: lighting and colour, and image segmentation.
Author: Derong Liu
Publisher: Springer
ISBN: 3540723935
Category : Computers
Languages : en
Pages : 1346
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Book Description
This book is part of a three volume set that constitutes the refereed proceedings of the 4th International Symposium on Neural Networks, ISNN 2007, held in Nanjing, China in June 2007. Coverage includes neural networks for control applications, robotics, data mining and feature extraction, chaos and synchronization, support vector machines, fault diagnosis/detection, image/video processing, and applications of neural networks.
Author: Kyoung Mu Lee
Publisher: Springer
ISBN: 364237431X
Category : Computers
Languages : en
Pages : 764
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Book Description
The four-volume set LNCS 7724--7727 constitutes the thoroughly refereed post-conference proceedings of the 11th Asian Conference on Computer Vision, ACCV 2012, held in Daejeon, Korea, in November 2012. The total of 226 contributions presented in these volumes was carefully reviewed and selected from 869 submissions. The papers are organized in topical sections on object detection, learning and matching; object recognition; feature, representation, and recognition; segmentation, grouping, and classification; image representation; image and video retrieval and medical image analysis; face and gesture analysis and recognition; optical flow and tracking; motion, tracking, and computational photography; video analysis and action recognition; shape reconstruction and optimization; shape from X and photometry; applications of computer vision; low-level vision and applications of computer vision.
Author: Zhangyang Wang
Publisher: Academic Press
ISBN: 0128136596
Category : Computers
Languages : en
Pages : 296
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Book Description
Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models-those that emphasize problem-specific Interpretability-with recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows how the toolkit of deep learning is closely tied with the sparse/low rank methods and algorithms, providing a rich variety of theoretical and analytic tools to guide the design and interpretation of deep learning models. The development of the theory and models is supported by a wide variety of applications in computer vision, machine learning, signal processing, and data mining. This book will be highly useful for researchers, graduate students and practitioners working in the fields of computer vision, machine learning, signal processing, optimization and statistics.
Author: Alfredo Petrosino
Publisher: Springer
ISBN: 3642411908
Category : Computers
Languages : en
Pages : 584
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Book Description
This book constitutes the refereed proceedings of the workshops held with the 17th International Conference on Image Analysis and Processing, ICIAP 2013, held in Naples, Italy, in September 2013. The proceedings include papers from the five individual workshops focusing on topics of interest to the pattern recognition, image analysis, and computer vision communities, exploring emergent research directions or spotlight cross-disciplinary links with related fields and / or application areas.
Author: E. R. Davies
Publisher: Academic Press
ISBN: 0128221496
Category : Technology & Engineering
Languages : en
Pages : 584
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Book Description
Advanced Methods and Deep Learning in Computer Vision presents advanced computer vision methods, emphasizing machine and deep learning techniques that have emerged during the past 5–10 years. The book provides clear explanations of principles and algorithms supported with applications. Topics covered include machine learning, deep learning networks, generative adversarial networks, deep reinforcement learning, self-supervised learning, extraction of robust features, object detection, semantic segmentation, linguistic descriptions of images, visual search, visual tracking, 3D shape retrieval, image inpainting, novelty and anomaly detection. This book provides easy learning for researchers and practitioners of advanced computer vision methods, but it is also suitable as a textbook for a second course on computer vision and deep learning for advanced undergraduates and graduate students. - Provides an important reference on deep learning and advanced computer methods that was created by leaders in the field - Illustrates principles with modern, real-world applications - Suitable for self-learning or as a text for graduate courses
Author: Yun Fu
Publisher: Springer
ISBN: 3319270044
Category : Technology & Engineering
Languages : en
Pages : 179
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Book Description
This book provides a unique view of human activity recognition, especially fine-grained human activity structure learning, human-interaction recognition, RGB-D data based action recognition, temporal decomposition, and causality learning in unconstrained human activity videos. The techniques discussed give readers tools that provide a significant improvement over existing methodologies of video content understanding by taking advantage of activity recognition. It links multiple popular research fields in computer vision, machine learning, human-centered computing, human-computer interaction, image classification, and pattern recognition. In addition, the book includes several key chapters covering multiple emerging topics in the field. Contributed by top experts and practitioners, the chapters present key topics from different angles and blend both methodology and application, composing a solid overview of the human activity recognition techniques.
