Visual Object Tracking from Correlation Filter to Deep Learning
Home > General > Visual Object Tracking from Correlation Filter to Deep Learning
Visual Object Tracking from Correlation Filter to Deep Learning

Visual Object Tracking from Correlation Filter to Deep Learning


     0     
5
4
3
2
1



International Edition


About the Book

Introduction.- Algorithm Foundations.- Correlation Filter Based Visual Object Tracking.- Correlation Filter with Deep Feature for Visual Object Tracking.- Deep Learning Based Visual Object Tracking.- Summary and Future Work.
About the Author: Weiwei Xing received the B.S. degree in computer science and technology and the Ph.D. degree in Signal and Information Processing from Beijing Jiaotong University, Beijing, China, in 2001 and 2006, respectively. She was also a visiting researcher at Department of Computer Science in University of Pennsylvania, PA, USA from Feb.2011 to Feb. 2012. She is currently a Professor at School of Software Engineering, Beijing Jiaotong University. Her research interests include video information processing, computer vison, machine learning, big data analysis and software engineering.

Weibin Liu received the Ph.D. degree in Signal and Information Processing from Institute of Information Science at Beijing Jiaotong University, China, in 2001. During 2001-2005, he was a researcher in Information Technology Division at Fujitsu Research and Development Center Co., LTD. Since 2005, he has been with the Institute of Information Science at Beijing Jiaotong University, where currently he is a professor in Digital Media Research Group. He was also a visiting researcher in Center for Human Modeling and Simulation at University of Pennsylvania, PA, USA during 2009-2010. His research interests include computer vision, computer graphics, image processing, virtual human and virtual environment, and pattern recognition.
Jun Wang received the M.S. degree in Pattern Recognition and Intelligent Systems from Hebei University, China, in 2015. He received the Ph.D degree in Signal and Information Processing from Institute of Information Science at Beijing Jiaotong University, China. He was also a visiting researcher in Visual Object Tracking at University of Central Florida, USA during 2018-2019. Currently, he is an associate professor at College of Electronic Information Engineering, Hebei University. His research interests include image processing, computer vision, visual object tracking and pattern recognition.
Shunli Zhang received the B.S. and M.S. degrees in electronics and information engineering from Shandong University, Jinan, China, in 2008 and 2011, respectively, and the Ph.D. degree in signal and information processing from Tsinghua University in 2016. He was a visiting scholar in Carnegie Mellon University, Pittsburgh, from 2018 to 2019. He is currently an associate professor in School of Software Engineering, BeijingJiaotong University. His research interests include pattern recognition, computer vision, and image processing.
Lihui Wang received the Ph.D. degree in Signal and Information Processing from Beijing Jiaotong University, Beijing, China, in 2011. She is currently a lecturer of the Department of Information and Communication, Army Academy of Armored Forces Academy. Her main research interests include computer application, big data analysis, and three-dimensional reconstruction.
Yuxiang Yang received the B.S. degree in computer science and technology from the Northeastern University of China, Liaoning, China, in 2014. He is currently a Ph.D. Candidate at School of Software Engineering, Beijing Jiaotong University. His research interests include deep learning, reinforcement learning, and object tracking.
Bowen Song received the B.S. degree in computer science and technology from the School of Computer Science and Technology, Heilongjiang University, China, in 2018. She is currently pursuing the master's degree at School of Software Engineering, Beijing Jiaotong University. Her research interests include visual object tracking, and deep learning.


Best Sellers



Product Details
  • ISBN-13: 9789811662447
  • Publisher: Springer Nature Singapore
  • Binding: Paperback
  • Language: English
  • Returnable: Y
  • Weight: 349 gr
  • ISBN-10: 9811662444
  • Publisher Date: 20 Nov 2022
  • Height: 234 mm
  • No of Pages: 193
  • Spine Width: 11 mm
  • Width: 156 mm


Similar Products

Add Photo
Add Photo

Customer Reviews

REVIEWS      0     
Click Here To Be The First to Review this Product
Visual Object Tracking from Correlation Filter to Deep Learning
Springer Nature Singapore -
Visual Object Tracking from Correlation Filter to Deep Learning
Writing guidlines
We want to publish your review, so please:
  • keep your review on the product. Review's that defame author's character will be rejected.
  • Keep your review focused on the product.
  • Avoid writing about customer service. contact us instead if you have issue requiring immediate attention.
  • Refrain from mentioning competitors or the specific price you paid for the product.
  • Do not include any personally identifiable information, such as full names.

Visual Object Tracking from Correlation Filter to Deep Learning

Required fields are marked with *

Review Title*
Review
    Add Photo Add up to 6 photos
    Would you recommend this product to a friend?
    Tag this Book Read more
    Does your review contain spoilers?
    What type of reader best describes you?
    I agree to the terms & conditions
    You may receive emails regarding this submission. Any emails will include the ability to opt-out of future communications.

    CUSTOMER RATINGS AND REVIEWS AND QUESTIONS AND ANSWERS TERMS OF USE

    These Terms of Use govern your conduct associated with the Customer Ratings and Reviews and/or Questions and Answers service offered by Bookswagon (the "CRR Service").


    By submitting any content to Bookswagon, you guarantee that:
    • You are the sole author and owner of the intellectual property rights in the content;
    • All "moral rights" that you may have in such content have been voluntarily waived by you;
    • All content that you post is accurate;
    • You are at least 13 years old;
    • Use of the content you supply does not violate these Terms of Use and will not cause injury to any person or entity.
    You further agree that you may not submit any content:
    • That is known by you to be false, inaccurate or misleading;
    • That infringes any third party's copyright, patent, trademark, trade secret or other proprietary rights or rights of publicity or privacy;
    • That violates any law, statute, ordinance or regulation (including, but not limited to, those governing, consumer protection, unfair competition, anti-discrimination or false advertising);
    • That is, or may reasonably be considered to be, defamatory, libelous, hateful, racially or religiously biased or offensive, unlawfully threatening or unlawfully harassing to any individual, partnership or corporation;
    • For which you were compensated or granted any consideration by any unapproved third party;
    • That includes any information that references other websites, addresses, email addresses, contact information or phone numbers;
    • That contains any computer viruses, worms or other potentially damaging computer programs or files.
    You agree to indemnify and hold Bookswagon (and its officers, directors, agents, subsidiaries, joint ventures, employees and third-party service providers, including but not limited to Bazaarvoice, Inc.), harmless from all claims, demands, and damages (actual and consequential) of every kind and nature, known and unknown including reasonable attorneys' fees, arising out of a breach of your representations and warranties set forth above, or your violation of any law or the rights of a third party.


    For any content that you submit, you grant Bookswagon a perpetual, irrevocable, royalty-free, transferable right and license to use, copy, modify, delete in its entirety, adapt, publish, translate, create derivative works from and/or sell, transfer, and/or distribute such content and/or incorporate such content into any form, medium or technology throughout the world without compensation to you. Additionally,  Bookswagon may transfer or share any personal information that you submit with its third-party service providers, including but not limited to Bazaarvoice, Inc. in accordance with  Privacy Policy


    All content that you submit may be used at Bookswagon's sole discretion. Bookswagon reserves the right to change, condense, withhold publication, remove or delete any content on Bookswagon's website that Bookswagon deems, in its sole discretion, to violate the content guidelines or any other provision of these Terms of Use.  Bookswagon does not guarantee that you will have any recourse through Bookswagon to edit or delete any content you have submitted. Ratings and written comments are generally posted within two to four business days. However, Bookswagon reserves the right to remove or to refuse to post any submission to the extent authorized by law. You acknowledge that you, not Bookswagon, are responsible for the contents of your submission. None of the content that you submit shall be subject to any obligation of confidence on the part of Bookswagon, its agents, subsidiaries, affiliates, partners or third party service providers (including but not limited to Bazaarvoice, Inc.)and their respective directors, officers and employees.

    Accept

    New Arrivals



    Inspired by your browsing history


    Your review has been submitted!

    You've already reviewed this product!