Gesture spotting for low-resolution sports video annotation

Myung Cheol Roh, Bill Christmas, Joseph Kittler, Seong Whan Lee

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)


Human gesture recognition plays an important role in automating the analysis of video material at a high level. Especially in sports videos, the determination of the player's gestures is a key task. In many sports views, the camera covers a large part of the sports arena, resulting in low resolution of the player's region. Moreover, the camera is not static, but moves dynamically around its optical center, i.e. pan/tilt/zoom camera. These factors make the determination of the player's gestures a challenging task. To overcome these problems, we propose a posture descriptor that is robust to shape corruption of the player's silhouette, and a gesture spotting method that is robust to noisy sequences of data and needs only a small amount of training data. The proposed posture descriptor extracts the feature points of a shape, based on the curvature scale space (CSS) method. The use of CSS makes this method robust to local noise, and our method is also robust to significant shape corruption of the player's silhouette. The proposed spotting method provides probabilistic similarity and is robust to noisy sequences of data. It needs only a small number of training data sets, which is a very useful characteristic when it is difficult to obtain enough data for model training. In this paper, we conducted experiments spotting serve gestures using broadcast tennis play video. From our experiments, for 63 shots of playing tennis, some of which include a serve gesture and while some do not, it achieved 97.5% precision rate and 86.7% recall rate.

Original languageEnglish
Pages (from-to)1124-1137
Number of pages14
JournalPattern Recognition
Issue number3
Publication statusPublished - 2008 Mar

Bibliographical note

Funding Information:
This research was supported by the Intelligent Robotics Development Program, one of the 21st Century Frontier R&D Programs funded by the Ministry of Commerce, Industry and Energy of Korea, the IST-507752 MUSCLE Network of Excellence, and the IST FP6-045547 VIDI-Video project.

Funding Information:
About the Author —MYUNG-CHEOL ROH received his B.S. degree in Computer Engineering from Kangwon University, Chun-Choen, Korea, in 2001, and his M.S. degrees in Computer Science and Engineering from Korea University, Seoul, Korea, in 2003. He is currently a Ph.D. student in the department of Computer Science and Engineering in Korea University. He won the best paper award of the 25th annual paper competition which is supervised by the Korea Information Science Society and is sponsored by Microsoft in 2006. He worked at the Center for Vision, Speech and Signal Processing in the University of Surrey as a collaborate researcher for 1 year since 2004. His present research interests include object tracking, text extraction, face and gesture recognition, robot vision and the pattern recognition related fields.


  • Gesture spotting
  • Low resolution video annotation
  • Posture descriptor
  • Posture determination

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


Dive into the research topics of 'Gesture spotting for low-resolution sports video annotation'. Together they form a unique fingerprint.

Cite this