Vision-Based Hand Gesture Recognition With Deep Machine Learning for Visual Servoing
Bibliographic record
Abstract
Vision based robot motion control (Visual Servoing) is a challenging research direction in utilizing vision information to control the robot motion. In this work, we successfully controlled the real-time pose of an end-effector based on a hand gesture detector that we trained with acquired training data in the lab environment. Meanwhile, a machine learning model for hand language translation based on convolutional neural network is proposed and utilized in this paper. SSD is the suggested meta-architecture that uses single feed-forward convolutional network for straightly predicting categories. The proposed model is evaluated on Tensorflow platform along with Pascal VOC 2012. In addition, an image-based vision servoing system based on Lyapunov’s theory is developed to control velocities of the robot’s joints. In the experimentation, the integration of the above systems and MobileNet network as a convolutional feature extractor shows good performance in identification, tracking and motion control of the robot. The model achieved 98% mAP and 0.8 for Total-loss while visual servoing also demonstrated good performance during experimentation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".