Name
Abstract | ||
|---|---|---|
In this work, a low-cost and efficient system is proposed to automatically analyze the halfpipe (HP) sports videos. In addition to the court color ratio information, we find the player region by using salient object detection mechanisms to face the challenge of motion blurred scenes in HP videos. Besides, a novel and efficient method for detecting the spin event is proposed on the basis of native motion vectors existing in a compressed video. Experimental results show that the proposed system is effective in recognizing the hard-to-be-detected spin events in HP videos. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2647868.2654883 | ACM Multimedia 2001 |
Keywords | Field | DocType |
sports video,vert ramp,action recognition,halfpipe,indexing methods | Computer vision,Broadcasting,Salient object detection,Computer graphics (images),Computer science,Action recognition,Artificial intelligence | Conference |
Citations | PageRank | References |
1 | 0.35 | 1 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hao-Kai Wen | 1 | 1 | 0.35 |
| Wei-Che Chang | 2 | 1 | 0.35 |
| Chia-hu Chang | 3 | 37 | 6.12 |
| Yin-Tzu Lin | 4 | 47 | 6.62 |
| Ja-ling Wu | 5 | 1569 | 168.11 |