Extending the Detection Range of Vision-Based Vehicular Instrumentation
Bibliographic record
Abstract
In this paper, we present a novel vision-based detection system able to extend the detection range of vehicles or mobile robots. The proposed system is used to detect and track moving targets from near-to-far ranges and covers a wide range of more than 130 m without decreasing detection accuracy. Typical examples of targets include traffic signs, vehicles, animals, and pedestrians. In this paper, the detection and tracking of moving pedestrians from near-to-far ranges is investigated. The proposed system is composed of two identical cameras. The first camera is equipped with a short focal length lens to detect and track pedestrians in near-to-mid range, and the second camera with a long focal length lens is used to detect and track pedestrians in mid-to-far range. To synchronize the detection results of both cameras and to eliminate repeated measurements, two synchronization algorithms were developed. The tracking process is applied after the detection, and it is used to track and predict the future motion and direction of pedestrian. To prevent vehicle-target collisions, two algorithms that generate alert and danger warnings are developed. A mathematical model based on the fundamental physics of the camera and lens is developed to illustrate the feasibility of our work. Finally, we conducted many experiments in large open-air parking lots and on Ottawa roads to show the applicability of our system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".