Autonomous three-dimensional tracking for reconfigurable active-vision-based object recognition
Bibliographic record
Abstract
Recognition algorithms would significantly benefit from object images acquired from preferential view points, e.g. unobstructed frontal views or complementary views. Active-vision systems, which are dynamically reconfigurable in an online mode, have been suggested in the literature as effective solutions for achieving this objective, namely, relocating cameras to obtain optimal visibility for a given situation. To obtain optimal visibility of an object of interest (OI), however, that OI's three-dimensional (3D) position and orientation (i.e. six degree-of-freedom pose) must be tracked in real time. Thus, this paper presents such an autonomous, real-time, six degree-of-freedom pose tracking system for a priori unknown objects. The proposed tracking method autonomously (a) selects the OI, (b) builds its approximate 3D model and uses this model to (c) track it in real time. As will be shown in this paper, via experimental results, the output of the proposed modeller can be effectively used by an active-vision system to relocate its cameras for effective preferential image acquisition. In the examples included herein, it will be noted that object visibility data obtained via camera reconfiguration based on the authors' ‘approximate’ tracking method are comparable with those obtainable based on ‘perfect’ OI tracking.
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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".