Autonomous fish tracking by ROV using Monocular Camera
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper concerns the autonomous tracking of fish using a Remotely Operated Vehicle (ROV) equipped with a single camera. An efficient image processing algorithm is presented that enables pose estimation of a particular species of fish - a Large Mouth Bass. The algorithm uses a series of filters including the Gabor filter for texture, projection segmentation, and geometrical shape feature extraction to find the fishes distinctive dark lines that mark the body and tail. Feature based scaling then produces the position and orientation of the fish relative to the ROV. By implementing this algorithm on each frame of a series of video frames, successive relative state estimates can be obtained which are fused across time via a Kalman Filter. Video taken from a VideoRay MicroROV operating within Paradise Lake, Ontario, Canada was used to demonstrate off-line fish state estimation. In the future, this approach will be integrated within a closed-loop controller that allows the robot to autonomously follow the fish and monitor its behavior.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it