TraViQuA: Natural Language Driven Traffic Video Querying Using Deep Learning
Bibliographic record
Abstract
Video cameras are widely utilized and have ingrained themselves into many aspects of our daily life.Analysis of video contents is more challenging as the size of the data collected from the cameras increases.The fundamental cause of this challenge is because certain data, like the videos, cannot be queried.Our research focuses on converting traffic videos into a structure that can be queried.Specifically, an application called TraViQuA was suggested f or natural language-based car search and localization in traffic videos.To query and identify cars, data including color, brand, and appearance time are used as features.The query is initiated in real time on live traffic feed, as the user enters the search term on the application interface.Our text to SQL conversion algorithm enables the mapping of a search term into a SQL query.Based on the response to the natural language query, TraViQuA can start the video from the relevant time.Deep neural networks were employed in our application for text to SQL conversion and feature extraction.Our research reveals that color and brand models had mean average precision of 98.714% and 91.742%, respectively.The text to SQL conversion had an 80% accuracy rate.To the best of our knowledge, TraViQuA is the first application that enables police officers to input a natural language description of a car and discover the car of interest that matches the description, bridging the gap in traffic video surveillance.Moreover, TraViQuA can be incorporated into other intelligent transportation systems to support law enforcement officials in urgent situations like hit-and-run incidents and amber alerts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".