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Author: Juan Andrei Villarroel Fernández
Publisher:
ISBN:
Category : Automatic tracking
Languages : en
Pages : 131
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Book Description
Abstract: "In this work we propose an algorithm that can achieve automatic segmentation and tracking of moving objects from a video sequence from a fixed camera. As a result of using multiple features, the output of the algorithm also provides with low-level descriptors of the segmented objects. These, together with other descriptors resulting from further processing of the objects extracted, can be used to create high-level descriptors of the video contents. This higher-level understanding of the scene enables for new high-level video-based applications to be built upon it. In the particular case of video libraries, it leads towards a more natural indexing of the video content, hence increasing accessibility in a searchable video system. The algorithm presented here integrates change detection with a multiple feature segmentation algorithm in two different ways that complement each other well. First, change detection is used as a spatial feature for segmentation. This provides the algorithm with a good hint about where to find the moving objects at each frame, so that they can be further segmented using the remaining features available. Second, change detection is used for temporal tracking of moving objects. By comparing motion-compensated change-detected pixels, we can achieve improved tracking of independent moving objects throughout a sequence of frames. Finally, using both strategies simultaneously results in a more stable segmentation and more reliable tracking of the moving objects."
Author: Juan Andrei Villarroel Fernández
Publisher:
ISBN:
Category : Automatic tracking
Languages : en
Pages : 131
Get Book Here
Book Description
Abstract: "In this work we propose an algorithm that can achieve automatic segmentation and tracking of moving objects from a video sequence from a fixed camera. As a result of using multiple features, the output of the algorithm also provides with low-level descriptors of the segmented objects. These, together with other descriptors resulting from further processing of the objects extracted, can be used to create high-level descriptors of the video contents. This higher-level understanding of the scene enables for new high-level video-based applications to be built upon it. In the particular case of video libraries, it leads towards a more natural indexing of the video content, hence increasing accessibility in a searchable video system. The algorithm presented here integrates change detection with a multiple feature segmentation algorithm in two different ways that complement each other well. First, change detection is used as a spatial feature for segmentation. This provides the algorithm with a good hint about where to find the moving objects at each frame, so that they can be further segmented using the remaining features available. Second, change detection is used for temporal tracking of moving objects. By comparing motion-compensated change-detected pixels, we can achieve improved tracking of independent moving objects throughout a sequence of frames. Finally, using both strategies simultaneously results in a more stable segmentation and more reliable tracking of the moving objects."
Author: Buyu Liu
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Automatic video parsing is a key step towards human-level dynamic scene understanding, and a fundamental problem in computer vision. A core issue in video understanding is to infer multiple scene properties of a video in an efficient and consistent manner. This thesis addresses the problem of holistic scene understanding from monocular videos, which jointly reason about semantic and geometric scene properties from multiple levels, including pixelwise annotation of video frames, object instance segmentation in spatio-temporal domain, and/or scene-level description in terms of scene categories and layouts. We focus on four main issues in the holistic video understanding: 1) what is the representation for consistent semantic and geometric parsing of videos? 2) how do we integrate high-level reasoning (e.g., objects) with pixel-wise video parsing? 3) how can we do efficient inference for multi-level video understanding? and 4) what is the representation learning strategy for efficient/cost-aware scene parsing? We discuss three multi-level video scene segmentation scenarios based on different aspects of scene properties and efficiency requirements. The first case addresses the problem of consistent geometric and semantic video segmentation for outdoor scenes. We propose a geometric scene layout representation, or a stage scene model, to efficiently capture the dependency between the semantic and geometric labels. We build a unified conditional random field for joint modeling of the semantic class, geometric label and the stage representation, and design an alternating inference algorithm to minimize the resulting energy function. The second case focuses on the problem of simultaneous pixel-level and object-level segmentation in videos. We propose to incorporate foreground object information into pixel labeling by jointly reasoning semantic labels of supervoxels, object instance tracks and geometric relations between objects. In order to model objects, we take an exemplar approach based on a small set of object annotations to generate a set of object proposals. We then design a conditional random field framework that jointly models the supervoxel labels and object instance segments. To scale up our method, we develop an active inference strategy to improve the efficiency of multi-level video parsing, which adaptively selects an informative subset of object proposals and performs inference on the resulting compact model. The last case explores the problem of learning a flexible representation for efficient scene labeling. We propose a dynamic hierarchical model that allows us to achieve flexible trade-offs between efficiency and accuracy. Our approach incorporates the cost of feature computation and model inference, and optimizes the model performance for any given test-time budget. We evaluate all our methods on several publicly available video and image semantic segmentation datasets, and demonstrate superior performance in efficiency and accuracy.
Author: Michael Ying Yang
Publisher: Academic Press
ISBN: 0128173599
Category : Technology & Engineering
Languages : en
Pages : 424
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Book Description
Multimodal Scene Understanding: Algorithms, Applications and Deep Learning presents recent advances in multi-modal computing, with a focus on computer vision and photogrammetry. It provides the latest algorithms and applications that involve combining multiple sources of information and describes the role and approaches of multi-sensory data and multi-modal deep learning. The book is ideal for researchers from the fields of computer vision, remote sensing, robotics, and photogrammetry, thus helping foster interdisciplinary interaction and collaboration between these realms. Researchers collecting and analyzing multi-sensory data collections – for example, KITTI benchmark (stereo+laser) - from different platforms, such as autonomous vehicles, surveillance cameras, UAVs, planes and satellites will find this book to be very useful. - Contains state-of-the-art developments on multi-modal computing - Shines a focus on algorithms and applications - Presents novel deep learning topics on multi-sensor fusion and multi-modal deep learning
Author: Giuseppe Valenzise
Publisher: Academic Press
ISBN: 0323986234
Category : Computers
Languages : en
Pages : 686
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Book Description
Get a broad overview of the different modalities of immersive video technologies—from omnidirectional video to light fields and volumetric video—from a multimedia processing perspective. From capture to representation, coding, and display, video technologies have been evolving significantly and in many different directions over the last few decades, with the ultimate goal of providing a truly immersive experience to users. After setting up a common background for these technologies, based on the plenoptic function theoretical concept, Immersive Video Technologies offers a comprehensive overview of the leading technologies enabling visual immersion, including omnidirectional (360 degrees) video, light fields, and volumetric video. Following the critical components of the typical content production and delivery pipeline, the book presents acquisition, representation, coding, rendering, and quality assessment approaches for each immersive video modality. The text also reviews current standardization efforts and explores new research directions. With this book the reader will a) gain a broad understanding of immersive video technologies that use three different modalities: omnidirectional video, light fields, and volumetric video; b) learn about the most recent scientific results in the field, including the recent learning-based methodologies; and c) understand the challenges and perspectives for immersive video technologies. - Describes the whole content processing chain for the main immersive video modalities (omnidirectional video, light fields, and volumetric video) - Offers a common theoretical background for immersive video technologies based on the concept of plenoptic function - Presents some exemplary applications of immersive video technologies
Author: Alexander J. Aved
Publisher:
ISBN:
Category :
Languages : en
Pages : 168
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Book Description
With heightened security concerns across the globe and the increasing need to monitor, preserve and protect infrastructure and public spaces to ensure proper operation, quality assurance and safety, numerous video cameras have been deployed. Accordingly, they also need to be monitored effectively and efficiently. However, relying on human operators to constantly monitor all the video streams is not scalable or cost effective. Humans can become subjective, fatigued, even exhibit bias and it is difficult to maintain high levels of vigilance when capturing, searching and recognizing events that occur infrequently or in isolation. These limitations are addressed in the Live Video Database Management System (LVDBMS), a framework for managing and processing live motion imagery data. It enables rapid development of video surveillance software much like traditional database applications are developed today. Such developed video stream processing applications and ad hoc queries are able to "reuse" advanced image processing techniques that have been developed. This results in lower software development and maintenance costs. Furthermore, the LVDBMS can be intensively tested to ensure consistent quality across all associated video database applications. Its intrinsic privacy framework facilitates a formalized approach to the specification and enforcement of verifiable privacy policies. This is an important step towards enabling a general privacy certification for video surveillance systems by leveraging a standardized privacy specification language. With the potential to impact many important fields ranging from security and assembly line monitoring to wildlife studies and the environment, the broader impact of this work is clear. The privacy framework protects the general public from abusive use of surveillance technology; success in addressing the "trust" issue will enable many new surveillance-related applications. Although this research focuses on video surveillance, the proposed framework has the potential to support many video-based analytical applications.
Author: Wee-Kheng Leow
Publisher: Springer
ISBN: 3540316787
Category : Computers
Languages : en
Pages : 686
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Book Description
It was our great pleasure to host the 4th International Conference on Image and Video Retrieval (CIVR) at the National University of Singapore on 20–22 July 2005. CIVR aims to provide an international forum for the discussion of research challenges and exchange of ideas among researchers and practitioners in image/video retrieval technologies. It addresses innovative research in the broad ?eld of image and video retrieval. A unique feature of this conference is the high level of participation by researchers from both academia and industry. Another unique feature of CIVR this year was in its format – it o?ered both the traditional oral presentation sessions, as well as the short presentation cum poster sessions. The latter provided an informal alternative forum for animated discussions and exchanges of ideas among the participants. We are pleased to note that interest in CIVR has grown over the years. The number of submissions has steadily increased from 82 in 2002, to 119 in 2003, and 125 in 2004. This year, we received 128 submissions from the international communities:with81(63.3%)fromAsiaandAustralia,25(19.5%)fromEurope, and 22 (17.2%) from North America. After a rigorous review process, 20 papers were accepted for oral presentations, and 42 papers were accepted for poster presentations. In addition to the accepted submitted papers, the program also included 4 invited papers, 1 keynote industrial paper, and 4 invited industrial papers. Altogether, we o?ered a diverse and interesting program, addressing the current interests and future trends in this area.
Author: Dan Schonfeld
Publisher: Springer Science & Business Media
ISBN: 3642128998
Category : Mathematics
Languages : en
Pages : 391
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Book Description
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.
Author: Giorgio Scibilia
Publisher:
ISBN:
Category :
Languages : en
Pages : 114
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Book Description
Author: James L. Crowley
Publisher: Springer Science & Business Media
ISBN: 3642239676
Category : Computers
Languages : en
Pages : 234
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Book Description
This book constitutes the refereed proceedings of the 8th International Conference on Computer Vision Systems, ICVS 2011, held in Sophia Antipolis, France, in September 2009. The 22 revised papers presented were carefully reviewed and selected from 58 submissions. The papers are organized in topical sections on vision systems, control of perception, performance evaluation, activity recognition, and knowledge directed vision.
Author: Henk Blanken
Publisher: Springer
ISBN: 3540451943
Category : Computers
Languages : en
Pages : 318
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Book Description
Recently, we have seen a steep increase in the popularity and adoption of XML, in areas such as traditional databases, e-business, the scientific environment, and on the web. Querying XML documents and data efficiently is a challenging issue; this book approaches search on XML data by combining content-based methods from information retrieval and structure-based XML query methods and presents the following parts: applications, query languages, retrieval models, implementing intelligent XML systems, and evaluation. To appreciate the book, basic knowledge of traditional database technology, information retrieval, and XML is needed. The book is ideally suited for courses or seminars at the graduate level as well as for education of research and development professionals working on Web applications, digital libraries, database systems, and information retrieval.
