Our goal is to spot words in silent speech videos without explicitly recognizing the spoken words, where the lip motion of the speaker is clearly visible and audio is absent. Existing work in this domain has mainly focused on recognizing a fixed set of words in word-segmented lip videos, which limits the applicability of the learned model due to limited vocabulary and high dependency on the model's recognition performance.
Yaghyavardha Singh Khangarot
Areas of Interest: Computer Vision, Machine Learning, Computational Cinematography
Address: CVIT, IIIT-H
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Areas of Interest: Computer Vision, Deep Learning, Sports Analytics
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]
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.
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