BV-Person: A Large-scale Dataset for Bird-view Person Re-identification
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Bibliographic record
Abstract
Person Re-IDentification (ReID) aims at re-identifying persons from non-overlapping cameras. Existing person ReID studies focus on horizontal-view ReID tasks, in which the person images are captured by the cameras from a (nearly) horizontal view. In this work we introduce a new ReID task, bird-view person ReID, which aims at searching for a person in a gallery of horizontal-view images with the query images taken from a bird's-eye view, i.e., an elevated view of an object from above. The task is important because there are a large number of video surveillance cameras capturing persons from such an elevated view at public places. However, it is a challenging task in that the images from the bird view (i) provide limited person appearance information and (ii) have a large discrepancy compared to the persons in the horizontal view. We aim to facilitate the development of person ReID from this line by introducing a large-scale real-world dataset for this task. The proposed dataset, named BV-Person, contains 114k images of 18k identities in which nearly 20k images of 7.4k identities are taken from the bird's-eye view. We further introduce a novel model for this new ReID task. Large-scale experiments are performed to evaluate our model and 11 current state-of-the-art ReID models on BV-Person to establish performance benchmarks from multiple perspectives. The empirical results show that our model consistently and substantially outperforms the state-of-the-art models on all five datasets derived from BV-Person. Our model also achieves state-of-the-art performance on two general ReID datasets. The BV-Person dataset is available at: https://git.io/BVPerson
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| 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 it