Real-Time Jaywalking Detection and Notification System using Deep Learning and Multi-Object Tracking
Bibliographic record
Abstract
Jaywalking refers to pedestrians walking or crossing in a roadway that is not dedicated to pedestrians. Due to illegal jaywalking, every year a lot of accidents happen worldwide that cause a significant amount of death and other physical injuries. Real-time jaywalking detection and notification systems can contribute to protecting vulnerable road users and increasing road safety. Many computer vision-based image processing techniques have been proposed to detect jaywalking including deep learning, motion path analysis, motion object segmentation, trajectory forecasting and position localization. However, these techniques are designed and evaluated for a single road area and have limited notification capability. In this paper, we propose a real-time multi-object tracking approach for jaywalking detection and notification that can be applied in multiple road areas simultaneously. We use the state-of-the-art deep learning model YOLOv4 and the multi-object tracking algorithm DeepSORT for real-time object detection and tracking, respectively. The notification component incorporates a novel vehicle-region pair matching algorithm based on the proximity of vehicles to the monitored region. Performance evaluation shows that our proposed approach can effectively detect jaywalking with 100% accuracy and provide push notifications to nearby vehicles in real-time.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".