3D-tracking of A Priori Unknown Objects in Cluttered Dynamic Environments
Bibliographic record
Abstract
Tracking of an object's full six degree-of-freedom (6-dof) position and orientation (pose) would allow a robotic system to autonomously perform a variety of complex tasks, such as docking from any preferred angle, surveillance of moving subjects, etc. Computer vision has been commonly advocated as an effective tool for 3D (i.e., 6-dof) tracking Objects of Interest (OIs). However, the vast majority of vision-based 6-dof pose trackers reported in the literature require a model of the OI to be provided a priori. Finding/selecting the OI to track is also essential to autonomous operation. A problem that has often been neglected. This Thesis proposes a novel, real-time object-tracking system that solves all of the aforementioned problems. The tracking procedure begins with OI selection. Since what constitutes an OI is application dependent, selection is achieved via a customizable framework of Interest Filters (IFs) that highlight regions of interest within an image. The region of greatest interest becomes the selected OI. Next, an approximate visual 3D model of the selected OI is built on-line by a real-time modeller. Unlike previously proposed techniques, this modeller can build the model of the OI even in the presence of background clutter; an essential task for tracking one object amongst many. Once a model is built, a real-time 6-dof tracker (i.e., the third sub-component) performs the actual 6-dof object tracking via 3D model projection and optical flow. Performing simultaneous modelling and tracking presents several challenges requiring novel solutions. For example, a novel data-reduction scheme based on colour-gradient redundancy is proposed herein that facilitates using colour input images whilst still maintaining real-time performance on current computer hardware. Likewise, a per-pixel occlusion-rejection scheme is proposed which enables tracking in the presence of partial occlusions. Various other techniques have also been developed within the framework of this Thesis in order to achieve real-time efficiency, robustness to lighting variations, ability to cope with high OI speeds, etc. Extensive experiments with both synthetic and real-world motion sequences have demonstrated the ability of the proposed object-tracking system to track a priori unknown objects. The proposed algorithm has also been tested within two target applications: autonomous convoying, and dynamic camera reconfiguration.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".