Multi-feature RGB-D generic object tracking using a simple filter hierarchy
Bibliographic record
Abstract
This research focuses on tracking generic non-rigid objects at close range to an infrared triangulation-based RGB-D sensor. The work was motivated by direct industry demand for a foundation for a low-cost application to operate in a surveillance setting. There are several novel components of this research that build on classical and state-of-the-art literature to extend into this real-world environment with limited constraints. The initialization is automatic with no a priori knowledge of the object and there are no restrictions on object appearance or transformation. There are no assumptions on object placement and only a very general physical model is applied to object trajectory. The tracking is performed using a Kalman filter and polynomial predictor to hypothesize the next location and a particle filter with colour, edge, depth edge, and absolute depth features to pinpoint object location. This work deals with challenges that are not explored in other work including highly variable object motion characteristics and generality with respect to the object tracked. It also explores the potential for multiple objects to occupy the same x-y location and have the same appearance. The result is a basic model for generic single object tracking that can be extended to any scenario with tailored occlusion-handling and augmented with behavioural analysis to confront a real-world problem.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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; both teacher heads agree on what is shown here.
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".