TennisVid2Text: Fine-grained Descriptions for Domain Specific Videos

Sukhwani, Mohak; Jawahar, C. V.

doi:10.5244/c.29.117

Cited by 4 publications

(5 citation statements)

References 27 publications

(54 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are also some works about fine-grained video captioning, including broadcasting for tennis videos [1], and Finegrained Sports Narrative dataset [2]. Models like LSTM-YT [10] and S2VT [11] have been used to complete those tasks.…”

Section: Fine-grained Video Captioningmentioning

confidence: 99%

See 1 more Smart Citation

Crowd Video Captioning

Yan,

Zhu,

2019

Preprint

View full text Add to dashboard Cite

Describing a video automatically with natural language is a challenging task in the area of computer vision. In most cases, the on-site situation of great events is reported in news, but the situation of the off-site spectators in the entrance and exit is neglected which also arouses people's interest. Since the deployment of reporters in the entrance and exit costs lots of manpower, how to automatically describe the behavior of a crowd of off-site spectators is significant and remains a problem. To tackle this problem, we propose a new task called crowd video captioning (CVC) which aims to describe the crowd of spectators. We also provide baseline methods for this task and evaluate them on the dataset WorldExpo'10. Our experimental results show that captioning models have a fairly deep understanding of the crowd in video and perform satisfactorily in the CVC task.

show abstract

Section: Fine-grained Video Captioningmentioning

confidence: 99%

“…In most great events, there are professional commentators in the stadium to broadcast the situation in real time, and some studies about automatic sports video commentary [1], [2] have been carried out. Outside the venue, entry and exit of spectators are also important.…”

Section: Introductionmentioning

confidence: 99%

Crowd Video Captioning

Yan,

Zhu,

2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Sports Understanding and Applications: Several researchers have worked on improving sports understanding using domain specific cues in the past [5,29]. Racket sports have received a lot of attention in this area with strides made in video summarization and highlight generation [9,10] and generating text descriptions [36]. Reno et al [28] proposed a platform for tennis which extract 3D ball trajectories using a specialized camera setup.…”

Section: Related Workmentioning

confidence: 99%

Towards Structured Analysis of Broadcast Badminton Videos

Ghosh

Singh

Jawahar

2018

2018 IEEE Winter Conference on Applications of Computer Vision (WACV)

View full text Add to dashboard Cite

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. We use commonly available broadcast videos of matches and, unlike previous approaches, does not rely on special camera setups or additional sensors.Our focus is on Badminton as the sport of interest. We propose a method to analyze a large corpus of badminton broadcast videos by segmenting the points played, tracking and recognizing the players in each point and annotating their respective badminton strokes. We evaluate the performance on 10 Olympic matches with 20 players and achieved 95.44% point segmentation accuracy, 97.38% player detection score (mAP@0.5), 97.98% player identification accuracy, and stroke segmentation edit scores of 80.48%. We further show that the automatically annotated videos alone could enable the gameplay analysis and inference by computing understandable metrics such as player's reaction time, speed, and footwork around the court, etc.

show abstract

“…strides made in video summarization and automatically generating highlights [3,?,? ], generating descriptions [10] and automatically segmenting coarse temporal scenes [5], annotating players [14,? ] and tracking the ball [13,?…”

Section: Related Workmentioning

confidence: 99%

SmartTennisTV: Automatic Indexing of Tennis Videos

Ghosh¹,

Jawahar²

2018

Communications in Computer and Information Science

View full text Add to dashboard Cite

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. We finally build an interface to effortlessly retrieve and view the relevant video segments by also automatically tagging the segmented rallies with human accessible tags such as 'fault' and 'deuce'. The efficiency of our approach is demonstrated on btv's from two major tennis tournaments.

show abstract

TennisVid2Text: Fine-grained Descriptions for Domain Specific Videos

Cited by 4 publications

References 27 publications

Crowd Video Captioning

Crowd Video Captioning

Towards Structured Analysis of Broadcast Badminton Videos

SmartTennisTV: Automatic Indexing of Tennis Videos

Contact Info

Product

Resources

About