Temporal Segmentation of Human Actions in Videos PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Temporal Segmentation of Human Actions in Videos PDF full book. Access full book title Temporal Segmentation of Human Actions in Videos by Alexander Richard. Download full books in PDF and EPUB format.
Author: Alexander Richard
Publisher:
ISBN:
Category : Video recordings
Languages : en
Pages :
Get Book Here
Book Description
Author: Alexander Richard
Publisher:
ISBN:
Category : Video recordings
Languages : en
Pages :
Get Book Here
Book Description
Author: Vignesh Saravanan Kannappan
Publisher:
ISBN:
Category :
Languages : en
Pages : 176
Get Book Here
Book Description
Author: Kuehne, Hildegard
Publisher: KIT Scientific Publishing
ISBN: 3731502828
Category : Electronic computers. Computer science
Languages : en
Pages : 208
Get Book Here
Book Description
This work focuses the recognition of complex human activities in video data. A combination of new features and techniques from speech recognition is used to realize a recognition of action units and their combinations in video sequences. The presented approach shows how motion information gained from video data can be used to interpret the underlying structural information of actions and how higher level models allow an abstraction of different motion categories beyond simple classification.
Author: Suman Saha
Publisher:
ISBN:
Category :
Languages : en
Pages : 194
Get Book Here
Book Description
Author: Rui Hou
Publisher:
ISBN:
Category :
Languages : en
Pages : 107
Get Book Here
Book Description
Automatic understanding of videos is one of the most active areas of computer vision research. It has applications in video surveillance, human computer interaction, video sports analysis, virtual and augmented reality, video retrieval etc. In this dissertation, we address four important tasks in video understanding, namely action recognition, temporal action localization, spatial-temporal action detection and video object/action segmentation. This dissertation makes contributions to above tasks by proposing. First, for video action recognition, we propose a category level feature learning method. Our proposed method automatically identifies such pairs of categories using a criterion of mutual pairwise proximity in the (kernelized) feature space, and a category-level similarity matrix where each entry corresponds to the one-vs-one SVM margin for pairs of categories. Second, for temporal action localization, we propose to exploit the temporal structure of actions by modeling an action as a sequence of sub-actions and present a computationally efficient approach. Third, we propose 3D Tube Convolutional Neural Network (TCNN) based pipeline for action detection. The proposed architecture is a unified deep network that is able to recognize and localize action based on 3D convolution features. It generalizes the popular faster R-CNN framework from images to videos. Last, an end-to-end encoder-decoder based 3D convolutional neural network pipeline is proposed, which is able to segment out the foreground objects from the background. Moreover, the action label can be obtained as well by passing the foreground object into an action classifier. Extensive experiments on several video datasets demonstrate the superior performance of the proposed approach for video understanding compared to the state-of-the-art.
Author: Yang Zhou
Publisher:
ISBN: 9781369057997
Category : High definition video recording
Languages : en
Pages : 108
Get Book Here
Book Description
Recently, fine-grained action analysis has raised a lot of research interests due to its potential applications in smart home, medical surveillance, daily living assist and child/elderly care, where action videos are captured indoor with fixed camera. Although background motion (i.e. one of main challenges for general action recognition) is more controlled compared to general action recognition, it is widely acknowledged that fine-grained action recognition is very challenging due to large intra-class variability, small inter-class variability, large variety of action categories, complex motions and complicated interactions. Fine-Grained actions, especially the manipulation sequences involve a large amount of interactions between hands and objects, therefore how to model the interactions between human hands and objects (i.e., context) plays an important role in action representation and recognition. We propose to discover the manipulated objects by human by modeling which objects are being manipulated and how they are being operated. Firstly, we propose a representation and classification pipeline which seamlessly incorporates localized semantic information into every processing step for fine-grained action recognition. In the feature extraction stage, we explore the geometric information between local motion features and the surrounding objects. In the feature encoding stage, we develop a semantic-grouped locality-constrained linear coding (SG-LLC) method that captures the joint distributions between motion and object-in-use information. Finally, we propose a semantic-aware multiple kernel learning framework (SA-MKL) by utilizing the empirical joint distribution between action and object type for more discriminative action classification. This approach can discover and model the inter- actions between human and objects. However, discovering the detailed knowledge of pre-detected objects (e.g. drawer and refrigerator). Thus, the performance of action recognition is constrained by object recognition, not to mention detection of objects requires tedious human labor for object annotation. Secondly, we propose a mid-level video representation to be suitable for fine-grained action classification. Given an input video sequence, we densely sample a large amount of spatio-temporal motion parts by temporal segmentation with spatial segmentation, and represent them with local motion features. The dense mid-level candidate parts are rich in localized motion information, which is crucial to fine-grained action recognition. From the candidate spatio-temporal parts, we perform an unsupervised approach to discover and learn the representative part detectors for final video representation. By utilizing the dense spatio-temporal motion parts, we highlight the human-object interactions and localized delicate motion in the local spatio-temporal sub-volume of the video. Thirdly, we propose a novel fine-grained action recognition pipeline by interaction part proposal and discriminative mid-level part mining. Firstly, we generate a large number of candidate object regions using off-the-shelf object proposal tool, e.g., BING. Secondly, these object regions are matched and tracked across frames to form a large spatio-temporal graph based on the appearance matching and the dense motion trajectories through them. We then propose an efficient approximate graph segmentation algorithm to partition and filter the graph into consistent local dense sub-graphs. These sub-graphs, which are spatio-temporal sub-volumes, represent our candidate interaction parts. Finally, we mine discriminative mid-level part detectors from the features computed over the candidate interaction parts. Bag-of-detection scores based on a novel Max-N pooling scheme are computed as the action representation for a video sample. Finally, we also focus on the first-view (egocentric) action recognition problem, which contains lots of hand-object interactions. On one hand, we propose a novel end-to-end trainable semantic parsing network for hand segmentation. On the other hand, we propose a second end-to-end deep convolutional network to maximally utilize the contextual information among hand, foreground object, and motion for interactional foreground object detection.
Author: Hildegard Kühne
Publisher:
ISBN: 9781013282911
Category : Technology & Engineering
Languages : en
Pages : 204
Get Book Here
Book Description
This work focuses the recognition of complex human activities in video data. A combination of new features and techniques from speech recognition is used to realize a recognition of action units and their combinations in video sequences. The presented approach shows how motion information gained from video data can be used to interpret the underlying structural information of actions and how higher level models allow an abstraction of different motion categories beyond simple classification. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.
Author: Andrew Fitzgibbon
Publisher: Springer
ISBN: 3642337120
Category : Computers
Languages : en
Pages : 901
Get Book Here
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: Munish Kumar
Publisher: MDPI
ISBN: 3036517146
Category : Technology & Engineering
Languages : en
Pages : 112
Get Book Here
Book Description
This book is to chart the progress in applying machine learning, including deep learning, to a broad range of image analysis and pattern recognition problems and applications. In this book, we have assembled original research articles making unique contributions to the theory, methodology and applications of machine learning in image analysis and pattern recognition.
Author: Nicoletta Noceti
Publisher: Springer Nature
ISBN: 3030467325
Category : Computers
Languages : en
Pages : 351
Get Book Here
Book Description
The new frontiers of robotics research foresee future scenarios where artificial agents will leave the laboratory to progressively take part in the activities of our daily life. This will require robots to have very sophisticated perceptual and action skills in many intelligence-demanding applications, with particular reference to the ability to seamlessly interact with humans. It will be crucial for the next generation of robots to understand their human partners and at the same time to be intuitively understood by them. In this context, a deep understanding of human motion is essential for robotics applications, where the ability to detect, represent and recognize human dynamics and the capability for generating appropriate movements in response sets the scene for higher-level tasks. This book provides a comprehensive overview of this challenging research field, closing the loop between perception and action, and between human-studies and robotics. The book is organized in three main parts. The first part focuses on human motion perception, with contributions analyzing the neural substrates of human action understanding, how perception is influenced by motor control, and how it develops over time and is exploited in social contexts. The second part considers motion perception from the computational perspective, providing perspectives on cutting-edge solutions available from the Computer Vision and Machine Learning research fields, addressing higher-level perceptual tasks. Finally, the third part takes into account the implications for robotics, with chapters on how motor control is achieved in the latest generation of artificial agents and how such technologies have been exploited to favor human-robot interaction. This book considers the complete human-robot cycle, from an examination of how humans perceive motion and act in the world, to models for motion perception and control in artificial agents. In this respect, the book will provide insights into the perception and action loop in humans and machines, joining together aspects that are often addressed in independent investigations. As a consequence, this book positions itself in a field at the intersection of such different disciplines as Robotics, Neuroscience, Cognitive Science, Psychology, Computer Vision, and Machine Learning. By bridging these different research domains, the book offers a common reference point for researchers interested in human motion for different applications and from different standpoints, spanning Neuroscience, Human Motor Control, Robotics, Human-Robot Interaction, Computer Vision and Machine Learning. Chapter 'The Importance of the Affective Component of Movement in Action Understanding' of this book is available open access under a CC BY 4.0 license at link.springer.com.
