Feature-based visual tracking for agricultural implements
Bibliographic record
Abstract
Systems which utilize implement-mounted cameras for machinery feedback, such as row crop cultivators and sectional sprayers, can be upgraded to provide high-accuracy ground speed and tracking data using visual tracking algorithms. Vector data produced by visual tracking can be incorporated into control systems to compensate for implement dynamics in complement with RTK-GNSS receivers and other sensors. Variations of the SURF, SIFT, and ORB feature-descriptor algorithms were evaluated using a dataset of 640×480 pixel videos on six surfaces (gravel, asphalt, grass, seedlings, residue, and pasture) for speeds from 1 to 5 m/s. Feature-descriptor matching of consecutive video frames was tested using two methods of the k-Nearest Neighbors (kNN) algorithm: (1) 1NN with cross-checking, and (2) 2NN with the ratio-test. Ground speed and tracking direction were calculated using a fast histogram filter to reject outliers between frames. Compared to RTK-GNSS, ORB with CLAHE pre-processing (CLORB) and 1NN cross-checking was found to be the most robust with respect to real-time applications. For 95% of measurements, CLORB achieved an error of 0.23 m/s. Similar accuracy was achieved with SURF, U-SURF, and SIFT, but CLORB was capable of producing vector data in real-time (approximately 25 Hz), whereas SURF, U-SURF, and SIFT were only capable of 15 Hz or less.
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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 | 0.000 |
| 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".