Detection of small moving objects using a moving camera
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 recent years, various background subtraction methods have been proposed and used in vision systems for moving object detection and tracking from moving cameras; however, most of them have difficulty in handling small and distant objects in complicated non-flat scenes. This paper presents a robust method to effectively segment moving objects from videos, captured by a camera on a moving platform. In our approach, a two-level registration is applied to estimate the effect of camera motion for motion compensation. After motion estimation and extraction of potential foreground pixels by Gaussian mixture model, noisy result is refined using component based and pixel based methods the latter of which uses the hidden markov model (HMM) for classifying pixels. Finally, foreground objects are tracked by a particle filter to exploit the temporal coherence of foreground motion and improve the detection accuracy through time. Experimental results show that our method outperforms competing methods for detecting moving objects in complex environments.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 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