Deep Learning Architectures for Face Recognition in Video Surveillance
Face recognition (FR) systems for video surveillance (VS) applications attempt to accurately detect the presence of target individuals over a distributed network of cameras. In video-based FR systems, facial models of target individuals are designed a pri
- PDF / 10,458,011 Bytes
- 237 Pages / 439.42 x 666.14 pts Page_size
- 31 Downloads / 274 Views
arning in Object Detection and Recognition
Deep Learning in Object Detection and Recognition
Xiaoyue Jiang • Abdenour Hadid • Yanwei Pang Eric Granger • Xiaoyi Feng Editors
Deep Learning in Object Detection and Recognition
123
Editors Xiaoyue Jiang School of Electronics and Information Northwestern Polytechnical University Xi’an, Shaanxi, China Yanwei Pang School of Electrical and Information Engineering Tianjin University Tianjin, Tianjin, China
Abdenour Hadid Center for Machine Vision and Signal Analysis University of Oulu Oulu, Oulu, Finland Eric Granger École de technologie supérieure University of Québec Montréal, QC, Canada
Xiaoyi Feng School of Electronics and Information Northwestern Polytechnical University Xi’an, Shanxi, China
ISBN 978-981-10-5151-7 ISBN 978-981-10-5152-4 (eBook) https://doi.org/10.1007/978-981-10-5152-4 © Springer Nature Singapore Pte Ltd. 2019 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore
Preface
Object detection and recognition are key components for most computer vision systems and determine the performance of the many applications, such as tracking, retrieval, video surveillance, and image captioning. The performance of object detection and recognition heavily depends on the quality of the extracted features and robustness of classifiers, since the appearance of images may be influenced by many factors like lighting conditions, the pose, the reflectance of objects, and the intrinsic characteristics of cameras. To achieve robust detection and recognition, the extracted features that are used for verification must be invariant to lighting, pose, and other transformations. In classical applications, the gradient-based fea
Data Loading...
