Bibliographic record
Abstract
With the rise of video datasets and self-driving cars, many industries seek a way to perform quick object detection on video, as well as perform predictive tracking on these objects. We propose a predictive video object detector (POD net) integrating the You Only Look Once v2 (YOLOv2) framework with the convolutional 2-dimensional (2D) Long Short Term Memory (LSTM) model proposed by Shi et al. Our POD net performs object detection using YOLOv2 and object prediction using the LSTM model in an iterative manner with a view to improve object detection in video streams via object prediction. In this study we present two different approaches that we implemented to predict objects in subsequent video clips. The first approach, PODv1, applies a post-temporal pattern matching mechanism wherein the YOLOv2 detector is used to detect objects in multiple images and the LSTM layer is used to perform temporal feature mapping across the output tensors of the detectors. The second approach, PODv2, provides better results by applying the temporal feature mapping first across the images and then feeding the output into the YOLOv2 detector which is wrapped using a Time Distributed layer. We tested POD net on the Multi-Object Tracking (MOT) 2017 dataset and the network was able to perform predictive object detection and tracking, demonstrating that the LSTM layer is useful for a variety of video analysis problems.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".