Dance Action Recognition and Pose Estimation Based on Deep Convolutional Neural Network
Bibliographic record
Abstract
Sports action recognition helps athletes correct their action range and standardize their poses. But it is not an easy task to recognize sports actions, due to the individual difference in action execution. Besides, the difficulty of action recognition increases with the diversity of actions and the complexity of background. The previous studies have not fully considered temporal changes, and failed to determine the exact staring point of actions. To solve the problem, this paper proposes a new method to recognize dance actions and estimate poses based on deep convolutional neural network (DCNN). Firstly, the authors presented full-effect expression of global and local features of dance actions, and derived an optimal model based on DeepPose. Next, a dance pose evaluation model was established based on time sequence segmentation network, and the sparse time sampling strategy was introduced to realize efficient and effective learning of the frame sequence of the whole video. Experimental results confirm the superiority of the full-effect expression of global and local features, and the effectiveness of the proposed model. The research results provide a reference for the application of deep learning (DL) in other scenarios of action recognition and pose estimation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".