Multiple Object Detection and Tracking in the Thermal Spectrum
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Multiple Object Tracking (MOT) is an integral part of machine vision research. Most tracking-by-detection based MOT solutions utilize video streams from RGB cameras for their operation. However, for real-world applications, it is necessary to utilize sensors that operate in different spectrums to accommodate for varying lighting conditions. Since object detection is the first step of the tracking pipeline in tracking-by-detection approaches, we compare the performance of state-of-the-art object detectors when trained on color images to their performance when trained on thermal images. We introduce a new dataset for multiple object tracking with thermal images and corresponding RGB images and show that state-of-the-art trackers perform better on thermal images, especially in poor lighting conditions. Finally, we propose the use of a dynamic cut-off thresh-old for tracking-by-detection approaches that factors the size of a predicted box to enhance the tracker association. Our dataset and source code are publicly available at https://github.com/wassimea/thermalMOT
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it