CVIT Home CVIT Home
  • Home
  • People
    • Faculty
    • Staff
    • PhD Students
    • MS Students
    • Alumni
    • Post-doctoral
    • Honours Student
  • Research
    • Publications
    • Thesis
    • Projects
    • Resources
  • Events
    • Talks and Visits
    • Major Events
    • Visitors
    • Summer Schools
  • Gallery
  • News & Updates
    • News
    • Blog
    • Newsletter
    • Banners
  • Contact Us
  • Login

anurag ghoshAnurag Ghosh

Areas of Interest: Computer Vision, Deep Learning, Sports Analytics
 
Email: This email address is being protected from spambots. You need JavaScript enabled to view it.
 
Address: CVIT, IIIT-H
 
Phone:
 
Personal Home Page: https://researchweb.iiit.ac.in/~anurag.ghosh

Publications

  • Anurag Ghosh, Suriya Singh and C.V. Jawahar -  Towards Structured Analysis of Broadcast Badminton Videos IEEE Winter Conference on Applications of Computer Vision (WACV 2018), Lake Tahoe, CA, USA, 2018 [PDF]

  • Anurag Ghosh and C. V. Jawahar -  SmartTennisTV: Automatic indexing of tennis videos National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), 2017 [PDF]

  • Anurag Ghosh, Yash Patel, Mohak Sukhwani and C.V. Jawahar - Dynamic Narratives for Heritage Tour 3rd Workshop on Computer Vision for Art Analysis (VisART), European Conference on Computer Vision (ECCV), 2016 [PDF]



Projects

relativeattributesTowards Structured Analysis of Broadcast Badminton Videos

People Involved :Anurag Ghosh, Suriya Singh and C. V. Jawahar

Sports video data is recorded for nearly every major tournament but remains archived and inaccessible to large scale data mining and analytics. It can only be viewed sequentially or manually tagged with higher-level labels which is time consuming and prone to errors. In this work, we propose an end-to-end framework for automatic attributes tagging and analysis of sport videos.

 

 
 
 

Smart-tennisSmartTennisTV: Automatic indexing of tennis videos

People Involved :Anurag Ghosh and C. V. Jawahar

In this paper, we demonstrate a score based indexing approach for tennis videos. Given a broadcast tennis video (BTV), we index all the video segments with their scores to create a navigable and searchable match. Our approach temporally segments the rallies in the video and then recognizes the scores from each of the segments, before refining the scores using the knowledge of the tennis scoring system

 

 
 
 

UdyanKhuranaUdyan Khurana

Areas of Interest: Computer Graphics, GPU Computing

Email: This email address is being protected from spambots. You need JavaScript enabled to view it.

Address: CVIT, IIIT-H
 
Phone:
 
Personal Home Page:

Publications



    Projects

    KalpitCThakkarKalpit C. Thakkar

    Areas of Interest: High Performance Computing, Graphics and Computer Vision

    Email: This email address is being protected from spambots. You need JavaScript enabled to view it.

    Address: CVIT, IIIT-H
     
    Phone:
     
    Personal Home Page:

    Publications



      Projects

      VARUNKUMARREDDYVarun Kumar Reddy Pirakala

      Areas of Interest: Deep Learning, Optimization Methods, Complexity Theory, Scene Text

      Email: This email address is being protected from spambots. You need JavaScript enabled to view it.

      Address: CVIT, IIIT-H
       
      Phone:
       
      Personal Home Page: https://researchweb.iiit.ac.in/~varun.pirakala

      Publications



        Projects

        HarishKrishnaHarish Krishna

        Areas of Interest: Deep Learning, Optimization Methods, Complexity Theory, Scene Text

        Email: This email address is being protected from spambots. You need JavaScript enabled to view it.

        Address: CVIT, IIIT-H
         
        Phone:
         
        Personal Home Page: https://researchweb.iiit.ac.in/~harishkrishna.v/

        Publications



          Projects

          More Articles …

          1. Bharat Lal Bhatnagar
          2. Kranthi Kumar Rachavarapu
          3. Ameya Prabhu
          4. Maneesh Bilalpur
          • Start
          • Prev
          • 1
          • 2
          • 3
          • 4
          • 5
          • 6
          • 7
          • 8
          • 9
          • 10
          • Next
          • End
          1. You are here:  
          2. Home
          3. People
          4. MS Students
          5. MS Students
          Bootstrap is a front-end framework of Twitter, Inc. Code licensed under MIT License. Font Awesome font licensed under SIL OFL 1.1.