Name
Abstract | ||
|---|---|---|
In volleyball matches, spiking is the most effective way to gain points, while blocking is the action to prevent the opponents from getting scores by spiking. In this paper, we propose an intelligent system for automatic spiking events detection and blocking pattern classification in real volleyball videos. First, the entire videos are segmented into clips of rallies by whistle detection. Then, we find the court region based on proper camera calibration, and detect the location of the net for judging the positions of spiking and blocking. Via analyzing the changes of moving pixels along the net, we make a bounding box around the blocking location, so as to classify the blocking patterns into two main categories based on the width of bounding box. Finally, two important tactic patterns, delayed spiking and alternate position spiking, are recognized. With the information of spiking events and blocking locations, we can collect the statistical data and make tactics inference easily. To the best of our knowledge, no previous work is focused on spiking or blocking event detection. The experimental results on the videos recorded by a university volleyball team are promising and demonstrate the effectiveness of our proposed scheme. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICME.2012.174 | ICME |
Keywords | Field | DocType |
volleyball match,university volleyball team,real volleyball video,event detection,blocking events,spiking event,alternate position spiking,volleyball videos,automatic spiking events detection,entire video,court region,delayed spiking,calibration,data acquisition,image segmentation,statistical analysis,accuracy,image classification,bounding box,sport,trajectory,camera calibration,image processing | Computer vision,Object detection,Pattern recognition,Computer science,Image processing,Image segmentation,Camera resectioning,Pixel,Artificial intelligence,Contextual image classification,Trajectory,Minimum bounding box | Conference |
Citations | PageRank | References |
2 | 0.37 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chun-Chieh Hsu | 1 | 16 | 5.22 |
| Hua-Tsung Chen | 2 | 289 | 28.72 |
| Chien-Li Chou | 3 | 86 | 10.09 |
| Suh-Yin Lee | 4 | 1596 | 319.67 |