Download or read book 3D Scene Understanding with Efficient Spatio temporal Reasoning written by JunYoung Gwak and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robust and efficient 3D scene understanding could enable embodied agents to safely interact with the physical world in real-time. The key to the remarkable success of computer vision in the last decade owes to the rediscovery of convolutional neural networks. However, this technology does not always directly translate to 3D due to the curse of dimensionality. The size of the data grows cubically with the voxels, and the same level of input resolution and network depth was infeasible compared to that of 2D. Based on the observation that the 3D space is mostly empty, sparse tensors and sparse convolutions stand out as an efficient and effective 3D counterparts to the 2D convolution by exclusively operating on non-empty spaces. Such efficiency gain supports deeper neural networks for higher accuracy in real-time reference speed. To this end, this thesis explores the application of sparse convolution to various 3D scene understanding tasks. This thesis breaks down a holistic 3D scene understanding pipeline into the following subgoals; 1. data collection from 3D reconstruction, 2. semantic segmentation, 3. object detection, and 4. multi-object tracking. With robotics applications in mind, this thesis aims to achieve better performance, scalability, and efficiency in understanding the high-level semantics of the spatio-temporal domain while addressing the unique challenges the sparse data poses. In this thesis, we propose generalized sparse convolution and demonstrate how our method 1. gains efficiency by leveraging the sparseness of the 3D point cloud, 2. achieves robust performance by utilizing the gained efficiency, 3. makes predictions on empty spaces by dynamically generating points, and 4. jointly solves detection and tracking with spatio-temporal reasoning. Altogether, this thesis proposes an efficient and reliable pipeline for a holistic 3D scene understanding.
Download or read book 3D Scene and Event Understanding by Joint Spatio temporal Inference and Reasoning written by Yuanlu Xu and published by . This book was released on 2019 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: It is a challenging yet crucial task to have a comprehensive understanding of human activities and events in the 3D scene. This task involves many many mid-level vision tasks (e.g., detection, tracking, pose estimation, action/interaction recognition) and requires high-level understandings and reasoning about their relations. In this dissertation, we aim to propose a novel and general framework for both mid-level and high-level tasks under this track, towards a better solution for complex 3D scene and event understanding. Specifically, we aim to formulate problems with interpretable representations, enforce high-level constraints with domain knowledge guided grammar, learn models solving multiple tasks jointly, and infer based on spatial, temporal and casual information. We make three major contributions in this dissertation: First, we introduce interpretable representations to incorporate high-level constraints defined by domain knowledge guided grammar. Specifically, we propose: i) Spatial and Temporal Attributed Parse Graph model (ST-APG) encoding compositionality and attribution for multi-view people tracking, enhancing trajectory associations across space and time, ii) Scene-centric Parse Graph to represent a coherent understanding of information obtained from cross-view scenes for multi-view knowledge fusion, iii) Fashion Grammar for constraining configurations of human appearance and clothing in human parsing, iv) Pose Grammar for describing physical and physiological relations among human body parts in human pose estimation, and v) Causal And-Or Graph (C-AOG) to represent the causal-effect relations between an object's fluent changes and involved activities in tracking interacting objects. Second, we formulate multiple related tasks into a joint learning, inference and reasoning framework for mutual benefits and better configurations, instead of solving each task independently. Specially, we propose: i) a joint parsing framework for iteratively tracking people locations and estimating people attributes, ii) a joint inference framework modeled by deep neural networks for passing messages from direct, top-down and bottom-up directions in the task of human parsing, and iii) a joint reasoning framework to reason object's fluent changes and track the object in videos, iteratively searching for a feasible causal graph structure. Third, we mitigate the problem of data scarcity and data-hungry model learning using a learning-by-synthesis framework. Given limited training samples, we consider either propagate supervisions to unpaired samples or synthesizing virtual samples that minimize discrepancies with the realistic data. Specifically, we develop a pose sample simulator to augment training samples in virtual camera views for the task of 3D pose estimation, which improves our model cross-view generalization ability. There are several interesting properties regarding the proposed frameworks: i) a novel perspective for problem formulation on joint inference and reasoning on space, time and causality, ii) overcoming the drawbacks of lack of interpretability and data hunger for end-to-end deep learning methods. Experiments show that our joint inference and reasoning framework outperforms existing approaches on many tasks and obtains more interpretable results.
Download or read book Representations and Techniques for 3D Object Recognition and Scene Interpretation written by Derek Hoiem and published by Morgan & Claypool Publishers. This book was released on 2011 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the grand challenges of artificial intelligence is to enable computers to interpret 3D scenes and objects from imagery. This book organizes and introduces major concepts in 3D scene and object representation and inference from still images, with a focus on recent efforts to fuse models of geometry and perspective with statistical machine learning. The book is organized into three sections: (1) Interpretation of Physical Space; (2) Recognition of 3D Objects; and (3) Integrated 3D Scene Interpretation. The first discusses representations of spatial layout and techniques to interpret physical scenes from images. The second section introduces representations for 3D object categories that account for the intrinsically 3D nature of objects and provide robustness to change in viewpoints. The third section discusses strategies to unite inference of scene geometry and object pose and identity into a coherent scene interpretation. Each section broadly surveys important ideas from cognitive science and artificial intelligence research, organizes and discusses key concepts and techniques from recent work in computer vision, and describes a few sample approaches in detail. Newcomers to computer vision will benefit from introductions to basic concepts, such as single-view geometry and image classification, while experts and novices alike may find inspiration from the book's organization and discussion of the most recent ideas in 3D scene understanding and 3D object recognition. Specific topics include: mathematics of perspective geometry; visual elements of the physical scene, structural 3D scene representations; techniques and features for image and region categorization; historical perspective, computational models, and datasets and machine learning techniques for 3D object recognition; inferences of geometrical attributes of objects, such as size and pose; and probabilistic and feature-passing approaches for contextual reasoning about 3D objects and scenes. Table of Contents: Background on 3D Scene Models / Single-view Geometry / Modeling the Physical Scene / Categorizing Images and Regions / Examples of 3D Scene Interpretation / Background on 3D Recognition / Modeling 3D Objects / Recognizing and Understanding 3D Objects / Examples of 2D 1/2 Layout Models / Reasoning about Objects and Scenes / Cascades of Classifiers / Conclusion and Future Directions
Download or read book Seeing the World Behind the Image written by Derek Hoiem and published by . This book was released on 2007 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "When humans look at an image, they see not just a pattern of color and texture, but the world behind the image. In the same way, computer vision algorithms must go beyond the pixels and reason about the underlying scene. In this dissertation, we propose methods to recover the basic spatial layout from a single image and begin to investigate its use as a foundation for scene understanding. Our spatial layout is a description of the 3D scene in terms of surfaces, occlusions, camera viewpoint, and objects. We propose a geometric class representation, a coarse categorization of surfaces according to their 3D orientations, and learn appearance-based models of geometry to identify surfaces in an image. These surface estimates serve as a basis for recovering the boundaries and occlusion relationships of prominent objects. We further show that simple reasoning about camera viewpoint and object size in the image allows accurate inference of the viewpoint and greatly improves object detection. Finally, we demonstrate the potential usefulness of our methods in applications to 3D reconstruction, scene synthesis, and robot navigation. Scene understanding from a single image requires strong assumptions about the world. We show that the necessary assumptions can be modeled statistically and learned from training data. Our work demonstrates the importance of robustness through a wide variety of image cues, multiple segmentations, and a general strategy of soft decisions and gradual inference of image structure. Above all, our work manifests the tremendous amount of 3D information that can be gleaned from a single image. Our hope is that this dissertation will inspire others to further explore how computer vision can go beyond pattern recognition and produce an understanding of the environment."
Download or read book Spatio temporal Reasoning for Semantic Scene Understanding and Its Application in Recognition and Prediction of Manipulation Actions in Image Sequences written by Fatemeh Ziaeetabar and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Human activity understanding has attracted much attention in recent years, due to a key role in a wide range of applications and devices, such as human- computer interfaces, visual surveillance, video indexing, intelligent humanoid robots, ambient intelligence and more. Of particular relevance, performing manipulation actions has a significant importance due to its enormous use, especially for service, as well as industrial robots. These robots strongly benefit from a fast and predictive recognition of manipulation actions. Although, for us as humans performing these actions is a quite triv...
Download or read book 3D Computer Vision written by Christian Wöhler and published by Springer Science & Business Media. This book was released on 2009-07-28 with total page 391 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work provides an introduction to the foundations of three-dimensional c- puter vision and describes recent contributions to the ?eld, which are of methodical and application-speci?c nature. Each chapter of this work provides an extensive overview of the corresponding state of the art, into which a detailed description of new methods or evaluation results in application-speci?c systems is embedded. Geometric approaches to three-dimensional scene reconstruction (cf. Chapter 1) are primarily based on the concept of bundle adjustment, which has been developed more than 100 years ago in the domain of photogrammetry. The three-dimensional scene structure and the intrinsic and extrinsic camera parameters are determined such that the Euclidean backprojection error in the image plane is minimised, u- ally relying on a nonlinear optimisation procedure. In the ?eld of computer vision, an alternative framework based on projective geometry has emerged during the last two decades, which allows to use linear algebra techniques for three-dimensional scene reconstructionand camera calibration purposes. With special emphasis on the problems of stereo image analysis and camera calibration, these fairly different - proaches are related to each other in the presented work, and their advantages and drawbacks are stated. In this context, various state-of-the-artcamera calibration and self-calibration methods as well as recent contributions towards automated camera calibration systems are described. An overview of classical and new feature-based, correlation-based, dense, and spatio-temporal methods for establishing point c- respondences between pairs of stereo images is given.
Download or read book Label Efficient 3D Scene Understanding written by David Griffiths and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Computer Vision ACCV 2014 written by Daniel Cremers and published by Springer. This book was released on 2015-04-15 with total page 722 pages. Available in PDF, EPUB and Kindle. Book excerpt: The five-volume set LNCS 9003--9007 constitutes the thoroughly refereed post-conference proceedings of the 12th Asian Conference on Computer Vision, ACCV 2014, held in Singapore, Singapore, in November 2014. The total of 227 contributions presented in these volumes was carefully reviewed and selected from 814 submissions. The papers are organized in topical sections on recognition; 3D vision; low-level vision and features; segmentation; face and gesture, tracking; stereo, physics, video and events; and poster sessions 1-3.
Download or read book Efficient 3D Scene Modeling and Mosaicing written by Tudor Nicosevici and published by Springer. This book was released on 2013-02-19 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book proposes a complete pipeline for monocular (single camera) based 3D mapping of terrestrial and underwater environments. The aim is to provide a solution to large-scale scene modeling that is both accurate and efficient. To this end, we have developed a novel Structure from Motion algorithm that increases mapping accuracy by registering camera views directly with the maps. The camera registration uses a dual approach that adapts to the type of environment being mapped. In order to further increase the accuracy of the resulting maps, a new method is presented, allowing detection of images corresponding to the same scene region (crossovers). Crossovers then used in conjunction with global alignment methods in order to highly reduce estimation errors, especially when mapping large areas. Our method is based on Visual Bag of Words paradigm (BoW), offering a more efficient and simpler solution by eliminating the training stage, generally required by state of the art BoW algorithms. Also, towards developing methods for efficient mapping of large areas (especially with costs related to map storage, transmission and rendering in mind), an online 3D model simplification algorithm is proposed. This new algorithm presents the advantage of selecting only those vertices that are geometrically representative for the scene.
Download or read book Analysis of Human centric Activities in Video Via Qualitative Spatio temporal Reasoning written by Hajar Sadeghi Sokeh and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applying qualitative spatio-temporal reasoning in video analysis is now a very active research topic in computer vision and artificial intelligence. Among all video analysis applications, monitoring and understanding human activities is of great interest. Many human activities can be understood by analysing the interaction between objects in space and time. Qualitative spatio-temporal reasoning encapsulates information that is useful for analysing huma-centric videos. This information can be represented in a very compact form involving interactions between objects of interest in the form of qualitative spatio-temporal relationships. This thesis focuses on three different aspects of interpreting human-centric videos; first introducing a representation of interactions between objects of interest, second determining which objects in the scene are relevant to the activity, and third recognising of human actions by applying the proposed representation model between human body joints and body parts. As a first contribution, we present an accurate and comprehensive model for representing several aspects of space over time from videos called "AngledCORE-9", a modified version of CORE-9 (proposed by Cohn et al. [2012]). This model is as efficient as CORE-9 and allows us to extract spatial information with much higher accuracy than previously possible. We evaluate our new knowledge representation method on a real video dataset to perform action clustering. Our next contribution is proposing a model for differentiating relevant from irrelevant objects to the human actions in the videos. The chief issue of recognising different human actions in videos using spatio-temporal features is that there are usually many moving objects in the scene. No existing method can successfully find the involved objects in the activity. The output of our system is a list of tracks for all possible objects in the video with their probabilities for being involved in the activity. The track with the highest probability is most likely to be the object with which the person is interacting. Knowing the involved object(s) in the activities is very advantageous. Since it can be used to improve the human action recognition rate. Finally, instead of looking at human-object interactions, we consider skeleton joints as the points of interest. Working on joints provides more information about how a person is moving to perform the activity. In this part of the thesis, we use videos with human skeletons in 3D captured by Kinect, MSR3D-action dataset. We use our proposed model "AngledCORE-9" to extract features and describe the temporal variation of these features frame by frame. We compare our results against some of the recent works on the same dataset.
Download or read book Computer Vision ECCV 2018 written by Vittorio Ferrari and published by Springer. This book was released on 2018-10-06 with total page 861 pages. Available in PDF, EPUB and Kindle. Book excerpt: The sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization; matching and recognition; video attention; and poster sessions.
Download or read book Computer Vision ECCV 2020 written by Andrea Vedaldi and published by Springer Nature. This book was released on 2020-11-06 with total page 795 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 30-volume set, comprising the LNCS books 12346 until 12375, constitutes the refereed proceedings of the 16th European Conference on Computer Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic. The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.
Download or read book Human like Holistic 3D Scene Understanding written by Siyuan Huang and published by . This book was released on 2021 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: Building an intelligent machine with human-like perception, interaction, learning, and reasoning remains a significant and challenging problem. Despite the recent remarkable progress in artificial intelligence, especially the deep learning techniques, we are still far from reaching this goal. Human intelligence exhibits unique advantages in learning to solve multiple tasks from limited data, acquiring skills and knowledge from interactions, learning efficiently with stages, and generalizing concepts to novel domains and environments. Merely combining individual algorithms without a human-centric architecture is hopeless for achieving such comprehensive capabilities. In this dissertation, we study the human-like holistic understanding in 3D scenes, which is the most related scenario to the real world. The core idea is to imitate the human's capability in perception, interaction, learning, and reasoning for solving holistic tasks. We first propose a framework for human-centric 3D scene parsing, reconstruction, and synthesis, focusing on integrating imagined humans into the perception system for interpreting the underlying human activities and intentions beyond the pixels. Then we describe several works on human-centric interaction understanding, including the human-object interactions and human-human interactions. Finally, we imitate the human-like learning and reasoning abilities by studying how to learn concepts with curriculum, design efficient closed-loop neural-grammar-symbolic learning algorithm, and build a concept learning framework that achieves systematic generalization.
Download or read book Efficient 3D Scene Modeling and Mosaicing written by Tudor Nicosevici and published by . This book was released on 2009 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Video Search and Mining written by Dan Schonfeld and published by Springer Science & Business Media. This book was released on 2010-05-22 with total page 391 pages. Available in PDF, EPUB and Kindle. Book excerpt: As cameras become more pervasive in our daily life, vast amounts of video data are generated. The popularity of YouTube and similar websites such as Tudou and Youku provides strong evidence for the increasing role of video in society. One of the main challenges confronting us in the era of information technology is to - fectively rely on the huge and rapidly growing video data accumulating in large multimedia archives. Innovative video processing and analysis techniques will play an increasingly important role in resolving the difficult task of video search and retrieval. A wide range of video-based applications have benefited from - vances in video search and mining including multimedia information mana- ment, human-computer interaction, security and surveillance, copyright prot- tion, and personal entertainment, to name a few. This book provides an overview of emerging new approaches to video search and mining based on promising methods being developed in the computer vision and image analysis community. Video search and mining is a rapidly evolving discipline whose aim is to capture interesting patterns in video data. It has become one of the core areas in the data mining research community. In comparison to other types of data mining (e. g. text), video mining is still in its infancy. Many challenging research problems are facing video mining researchers.
Download or read book Efficient Structured Prediction for Visual Scene Understanding written by Jian Yao and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis focuses on three topics in visual scene understanding, sorted from low level to high level: unsupervised segmentation, collision-free space detection and holistic scene understanding. All of them are modeled within the framework of probabilistic graphical models. We tackle the problem of unsupervised segmentation in the form of superpixels, with the emphasis on speed and accuracy. To this end, a real-time coarse-to-fine topologically preserving segmentation algorithm [188] is proposed. In this algorithm, we define the problem as a markov random field (MRF) which preserves the boundary in the image and the topology of superpixels. We propose a coarse to fine optimization technique that speeds up the inference dramatically without sacrificing the accuracy for both static images and stereo images. This thesis next tackles the problem of the drivable collision-free space from a monocular video [189]. In contrast to previous approaches that use stereo cameras or LIDAR, we show a method to solve this problem using a single camera. The free space estimation is reduced to an inference problem on a 1D graph, where each node represents a column in the image and its label denotes a position that separates the free space from obstacles. We exploit both spatial and temporal features to define potential functions on the 1D graph, whose parameters are learned through structured SVM. The inference on the 1D graph can be efficiently and exactly solved by dynamic programming. Lastly this thesis interprets the scene by performing scene classification, object detection and semantic segmentation. Rather than solving these problems independently, the proposed method [187] is based on a holistic model which jointly reasons about three individual problems as a whole within a conditional random field (CRF). Learning and inference in the model are efficient as we reason at the segment level instead of pixel level, and introduce auxiliary variables that allow us to decompose the inherent high-order potentials into pairwise potentials between a few variables with small number of states (at most the number of classes). The experimental results show that the joint model has better performance than solving each problem independently.
Download or read book Robot Intelligence Technology and Applications 7 written by Jun Jo and published by Springer Nature. This book was released on 2023-02-28 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: We are starting to enter a post-COVID-19 life. While this pandemic has made everyone’s life challenging, it also expedited the transition of our everyday lives into a new form, often called “The New Normal.” Although many people often use the terminology, perhaps we still do not have consensus about what it is and what it should be like. However, one thing that is clear namely that robotics and artificial intelligence technologies are playing a critical role in this transition phase of our everyday lives. We saw the emergence of last-mile delivery robots on the street, AI-embedded service robots in restaurants, uninhabited shops, non-face-to-face medical services, conferences and talks in metaverses, and AI-based online education programs. This book is an edition that aims at serving researchers and practitioners in related fields with a timely dissemination of the recent progress in the areas of robotics and artificial intelligence. This book is based on a collection of papers presented at the 10th International Conference on Robot Intelligence Technology and Applications (RiTA), held at Griffith University in the Gold Coast, Queensland, Australia. The conference was held in a hybrid format on December 7–9, 2022, with the main theme “Artificial, Agile, Acute Robot Intelligence.” For better readability, the total of 41 papers are grouped into five chapters: Chapter I: Motion Planning and Control; Chapter II: Vision and Image Processing; Chapter III: Unmanned Aerial Vehicles and Autonomous Vehicles; Chapter IV: Learning and Classification; and Chapter V: Environmental and Societal Robotic Applications. The articles were accepted through a rigorous peer-review process and presented at the RiTA 2022 conference. Also, they were updated, and final versions of the manuscripts were produced after in-depth discussions during the conference. We would like to thank all the authors and editors for contributing to this edition.
