View adaptive multi-object tracking method based on depth relationship cues
Why this work is in the frame
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Bibliographic record
Abstract
Multi-object tracking (MOT) tasks face challenges from multiple perception views due to the diversity of application scenarios. Different views (front-view and top-view) have different imaging and data distribution characteristics, but the current MOT methods do not consider these differences and only adopt a unified association strategy to deal with various occlusion situations. This paper proposed View Adaptive Multi-Object Tracking Method Based on Depth Relationship Cues (ViewTrack) to enable MOT to adapt to the scene's dynamic changes. Firstly, based on exploiting the depth relationships between objects by using the position information of the bounding box, a view-type recognition method based on depth relationship cues (VTRM) is proposed to perceive the changes of depth and view within the dynamic scene. Secondly, by adjusting the interval partitioning strategy to adapt to the changes in view characteristics, a view adaptive partitioning method for tracklet sets and detection sets (VAPM) is proposed to achieve sparse decomposition in occluded scenes. Then, combining pedestrian displacement with Intersection over Union (IoU), a displacement modulated Intersection over Union method (DMIoU) is proposed to improve the association accuracy between detection and tracklet boxes. Finally, the comparison results with 12 representative methods demonstrate that ViewTrack outperforms multiple metrics on the benchmark datasets. The code is available at https://github.com/Hamor404/ViewTrack .
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it