Disentanglement-Based Multi-Vehicle Detection and Tracking for Gate-Free Parking Lot Management
Bibliographic record
Abstract
Multiple object tracking (MOT) techniques can help to build gate-free parking lot management systems purely under vision-based surveillance. However, conventional MOT methods tend to suffer long-term occlusion and cause ID switch problems, making applying them directly in crowded and complex parking lot scenes challenging. Hence, we present a novel disentanglement-based architecture for multi-object detection and tracking to relieve the ID switch issues. First, a background image is disentangled from the original input frame; then, MOT is applied separately to the background and original frames. Next, we design a fusion strategy that can solve the ID switch problem by keeping track of the occluded vehicles while considering complex interactions among vehicles. In addition, we provide a dataset with annotations in severe occlusions parking lot scenes that suits the application. The experiment results show our superiority over the state-of-the-art trackers quantitatively and qualitatively.
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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 | 0.000 |
| 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 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".