Real-Time Multiple Object Tracking for Safe Cooking Activities
Bibliographic record
Abstract
Abstract This work presents a real-time system for tracking multiple object in the context of meal preparation when using the Cognitive Orthosis for CoOKing (COOK). This system is called SafeCOOK. It aims to provide more capabilities to detect some dangerous situations that the current system does not consider. For example, it can locate a utensil or other kitchen object that has been left on the cooking surface of the stove while a meal is being prepared. This system uses a hybrid method based on YOLO and KCF to detect, track and drop cooking utensils as they enter and leave the cooking area, and is capable of monitoring an entire cooktop in real-time with a single camera. The software has been implemented on an embedded platform in the smart stove and has been added to it. The system produces good segmentation and tracking results at a frame rate of 1 to 4 frames per second, as demonstrated in extensive experiments using video sequences under different conditions.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".