Deep learning‐based real‐time fine‐grained pedestrian recognition using stream processing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Real‐time recognition of pedestrian details can be very important in emergency situations for security reasons, such as traffic accidents identification from traffic video. However, this is challenging due to the needed accuracy of video data mining, and also the performance for real‐time video processing. Here, the authors propose a solution for fine‐grained pedestrian recognition in monitoring scenarios using deep learning and stream processing cloud computing, which is called DRPRS (deep learning‐based real‐time fine‐grained pedestrian recognition using stream processing). The authors design an improved convolutional neural network (CNN) network called fine‐CNN, which is a nine‐layer neural network for detailed pedestrian recognition. In DRPRS, a pedestrian in a surveillance video is segmented and fine‐grainedly recognised using improved single‐shot detector and several fine‐CNNs. DRPRS is supported by parallel mechanisms provided by Apache Storm stream processing framework. In addition, in order to further improve the recognition performance, a GPU‐based scheduling algorithm is proposed to make full use of GPU resources in a cluster. The whole recognition process is deployed on a big video data processing platform to meet real‐time requirements. DRPRS is extensively evaluated in terms of accuracy, fault tolerance, and performance, which show that the proposed approach is efficient.
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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 | 0.002 | 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 | 0.000 | 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