Classifying Pedestrian Actions In Advance Using Predicted Video Of Urban Driving Scenes
Bibliographic record
Abstract
Fig. 1.Generating predictions of a future for a pedestrian attempting to cross the street. We pick out two key frames from the (a) input sequence and the (b) ground truth sequence, 16 frames apart. Image (c) shows our prediction at the same time instant as the ground truth.We explore prediction of urban pedestrian actions by generating a video future of the traffic scene, and show promising results in classifying pedestrian behaviour before it is observed. We compare several encoder-decoder network models that predict 16 frames (400-600 milliseconds of video) from the preceding 16 frames. Our main contribution is a method for learning a sequence of representations to iteratively transform features learnt from the input to the future. Then we use a binary action classifier network for determining a pedestrian's crossing intent from predicted video. Our results show an average precision of 81%, significantly higher than previous methods. The model with the best classification performance runs for 117 ms on commodity GPU, giving an effective look-ahead of 416 ms.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".