Advanced surveillance systems: combining video and thermal imagery for pedestrian detection
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
In the current context of increased surveillance and security, more sophisticated surveillance systems are needed. One idea relies on the use of pairs of video (visible spectrum) and thermal infrared (IR) cameras located around premises of interest. To automate the system, a dedicated image processing approach is required, which is described in the paper. The first step in the proposed study is to collect a database of known scenarios both indoor and outdoor with a few pedestrians. These image sequences (video and TIR) are synchronized, geometrically corrected and temperature calibrated. The next step is to develop a segmentation strategy to extract the regions of interest (ROI) corresponding to pedestrians in the images. The retained strategy exploits the motion in the sequences. Next, the ROIs are grouped from image to image separately for both video and TIR sequences before a fusion algorithm proceeds to track and detect humans. This insures a more robust performance. Finally, specific criteria of size and temperature relevant to humans are introduced as well. Results are presented for a few typical situations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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