Sliding Window Check: Repairing Object Identities
Bibliographic record
Abstract
Real-time Multiple Object Tracking (MOT) solutions are popular due to their potential in various tasks. Most real-time oriented solutions use a single model for detection and tracking, and perform pairwise comparisons using past and present features to track objects in time. We find that using a single frame of data is not robust. Some algorithms learn appearance data by relying on additional networks, leading to structures unsuited for real-time performance. By storing multiple past features with a temporal sliding window and performing multiple optimized pairwise comparisons using the window, we are able to improve tracking performance at a small computational overhead. We do so by introducing a novel repair step to the association algorithm. We propose Sliding Window Check (SWC), a tracking algorithm which can be applied to many state-of-the-art one-shot trackers with consistent improvements to HOTA and IDF1 on MOT17 and MOT20. We also address the lack of track-repair algorithms, and concerns about MOTA penalizing track-repairing algorithms. Comparisons against 4 recent works show the efficacy of SWC.
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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 | 0.001 | 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 | 0.001 | 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".