Pedestrian Detection Using MB-CSP Model and Boosted Identity Aware Non-Maximum Suppression
Bibliographic record
Abstract
Pedestrian detection is an important task in autonomous surveillance systems. Despite the rapid progress in pedestrian detection field, detecting occluded pedestrians remains a challenging task due to the great variations in occluded pedestrians appearance and the drastic loss of pedestrian information in some severe cases. In this paper, we tackle the occlusion problem by proposing a multi-branch pedestrian detection model based on center and scale prediction framework. The proposed model employs features extracted from full pedestrian’s body as well as its upper, middle, and lower body parts using four detection branches. This multi-branch approach ensures that data representing the true pedestrian appearances, whether they are partially or completely visible, can dominate the final decision-making, minimizing the interference of non-pedestrian data in the detection. Furthermore, to implement the proposed model, the visibility of different pedestrian parts is appropriately annotated, which facilitates the training process. The final decision is made based on the four MB-CSP branches outputs, using a proposed fusing method, named Boosted Identity Aware-Non Maximum Suppression. On heavy occlusion settings, the proposed model resulted in the miss rates of 27.83%, 47.29% and 33.3% for Caltech-USA, Citypersons and EuroCity Persons datasets, respectively.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".