Person Following Robot Using Selected Online Ada-Boosting with Stereo Camera
Bibliographic record
Abstract
Person following behavior is an important task for social robots. To enable robots to follow a person, we have to track the target in real-time without critical failures. There are many situations where the robot will potentially loose tracking in a dynamic environment, e.g., occlusion, illumination, pose-changes, etc. Often, people use a complex tracking algorithm to improve robustness. However, the trade-off is that their approaches may not able to run in real-time on mobile robots. In this paper, we present Selected Online Ada-Boosting (SOAB) technique, a modified Online Ada-Boosting (OAB) tracking algorithm with integrated scene depth information obtained from a stereo camera which runs in real-time on a mobile robot. We build and share our results on the performance of our technique on a new stereo dataset for the task of person following. The dataset covers different challenging situations like squatting, partial and complete occlusion of the target being tracked, people wearing similar clothes, appearance changes, walking facing the front and back side of the person to the robot, and normal walking.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".