Name
Title | ||
|---|---|---|
High Performance Moves Recognition And Sequence Segmentation Based On Key Poses Filtering |
Abstract | ||
|---|---|---|
We present a discriminative key pose-based approach for moves recognition and segmentation of training sequences for high performance sports. Compared to daily human gestures, moves in high performance sports are faster and have low inter-class variability, which produce noisy features and ambiguity. Our approach combines a robust filtering strategy to select frames composed of discriminative poses (key poses) and the discriminative Latent-Dynamic Conditional Random Fields (LDCRF) model to predict a label for each frame from the training sequence. We evaluate our approach on unsegmented sequences of Taekwondo training. Experimental results indicate that our methodology outperforms the Decision Forests method in terms of efficiency and accuracy. Our average recognition rate was equal to 74.72% while Decision Forests achieves 58.29%. The experiments also show that our approach was able to recognize and segment high speed moves like roundhouse kicks, which can reach peak linear speeds up to 26 m/s. |
Year | Venue | Field |
|---|---|---|
2016 | 2016 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2016) | Conditional random field,Computer vision,Pattern recognition,Computer science,Gesture,Segmentation,Filter (signal processing),Feature extraction,Artificial intelligence,Hidden Markov model,Discriminative model,Ambiguity |
DocType | ISSN | Citations |
Conference | 2472-6737 | 2 |
PageRank | References | Authors |
0.39 | 20 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Claudio Marcio de Souza Vicente | 1 | 2 | 0.39 |
| Erickson R. Do Nascimento | 2 | 265 | 23.10 |
| Luiz Eduardo C. Emery | 3 | 2 | 0.39 |
| Cristiano Arruda G. Flor | 4 | 2 | 0.39 |
| Thales Vieira | 5 | 96 | 8.25 |
| Leonardo B. Oliveira | 6 | 554 | 30.22 |