Person Re-Identification by Discriminative Local Features of Overlapping Stripes
Bibliographic record
Abstract
The human visual system can recognize a person based on his physical appearance, even if extreme spatio-temporal variations exist. However, the surveillance system deployed so far fails to re-identify the individual when it travels through the non-overlapping camera’s field-of-view. Person re-identification (Re-ID) is the task of associating individuals across disjoint camera views. In this paper, we propose a robust feature extraction model named Discriminative Local Features of Overlapping Stripes (DLFOS) that can associate corresponding actual individuals in the disjoint visual surveillance system. The proposed DLFOS model accumulates the discriminative features from the local patch of each overlapping strip of the pedestrian appearance. The concatenation of histogram of oriented gradients, Gaussian of color, and the magnitude operator of CJLBP bring robustness in the final feature vector. The experimental results show that our proposed feature extraction model achieves rank@1 matching rate of 47.18% on VIPeR, 64.4% on CAVIAR4REID, and 62.68% on Market1501, outperforming the recently reported models from the literature and validating the advantage of the proposed model.
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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) | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".