People Tracking using Robust Motion Detection and Estimation
Bibliographic record
Abstract
Real world computer vision systems highly depend on reliable, robust retrieval of motion cues to make accurate decisions about their surroundings. In this paper, we present a simple, yet high performance low-level filter for motion tracking in digitized video signals. The algorithm is based on constant characteristics of a common, 2-frame interlaced video signal, yet results presented in this paper show its applicability to highly compressed, noisy image sequences as well. In general, our approach uses a computationally low-cost solution to define the area of interest for tracking of multiple, moving objects. Despite its simplicity, it compares very well to existing approaches due to its robustness towards environmental changes. To demonstrate this, we present results of processing a sequence of JPEG-compressed monocular images of a parking lot in order to track pedestrians, cars and bicycles. Despite a high level of noise and changing lighting conditions, the algorithm successfully segments a moving object and tracks its position along a trajectory.
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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 | 0.000 | 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 | 0.001 |
| 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 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".