POPD: Partial Occluded Pedestrian Detection Using A Multimodal Deep Learning Approach
Bibliographic record
Abstract
With the enhancement in the growth of the automotive industry, pedestrian detection is one of the major issues to prevent collisions on roads. This also plays a significant role in various applications ranging from road safety to urban planning. Hence, in this paper, a multimodal deep learning scheme based Partial Occluded Pedestrian Detection (POPD) approach has been proposed. Primarily, data fusion has been performed by considering multimodal data from different sources for effective implementation of pedestrian detection. In the next step, a key-point detection algorithm was applied to this aggregated data, and Mask-RCNN was deployed to classify different occlusion profiles. To evaluate results, extensive simulations have been performed and exhibited results show that the proposed scheme has provided satisfactory results when compared with benchmark schemes. The findings of this study show a considerable increase in terms of detection accuracy, particularly in situations with a lot of occlusions.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".