Accurate Detection and Localization of Individual Free Street Parking Spaces Using AI and Innovative Global Motion Estimation
Bibliographic record
Abstract
Traffic congestion has become a universal phenomenon in metropolitan cities. The prevalence of congested roads not only results in delays, increased fuel consumption, environmental pollution, and a reduced quality of life, but also contributes to lowered productivity due to the time wasted in the search for available street parking. Current solutions are either tailored solely for detecting vacant spaces within parking lots or rely on unfeasible street sensors and stationary surveillance cameras, falling short in accurately pinpointing individual free street parking spots. In this paper, we introduce an innovative street parking detection and localization system designed to cater to both human-driven and autonomous vehicles. Our solution is specifically engineered to be integrated into modern vehicles, utilizing the live video feed from the car's built-in navigation and obstacle avoidance cameras. This approach combines convolutional neural networks, our video global motion analysis, and an innovative distance measurement technique to achieve accurate detection and precise localization of unoccupied street parking spaces. Our solution eliminates the impact of vehicle speed, duplicate detection of identical parking spots during sudden stops, and delivers outstanding accuracy while maintaining low computational costs and complexity. We also introduce a unique dataset tailored for this objective, serving as the cornerstone for training and assessing well established object detection network architectures. Performance evaluations confirm the proposed method's efficacy across all types of scenarios.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".