Real-Time Person Re-Identification Using Omni-Scale Feature Learning Network and Yolov5: A Comparative Study
Bibliographic record
Abstract
Video-based person re-identification seeks to match video footage of an individual across non-overlapping multi-camera systems in real-time, probing for instances of the same identity appearing at different locations and times.The critical process in video-based person re-identification involves feature aggregation from the video track.This study introduces a method utilizing a convolutional neural network model named Omni-Scale Feature Learning Network (OSNet) for video-based re-identification.The performance of this method is evaluated on the large-scale MARS dataset and compared with other network models.Furthermore, a novel approach using You Only Look Once version 5 (Yolov5) is proposed for the first time for image, video, and real-time person detection and reidentification.This approach was trained on a custom-created dataset, gathered from two cameras capturing multiple identities of Computer Engineering students at the University of Basrah.The proposed method yielded promising results, with a re-identification accuracy of 80%.The aim of this work is to establish a real-time person re-identification system using the Yolov5 algorithm, and to contrast its performance with that of the OSNet.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 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".