VIEW-INVARIANT HUMAN ACTIVITY RECOGNITION BASED ON SHAPE AND MOTION FEATURES
Why this work is in the frame
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Bibliographic record
Abstract
Recognizing human activities from image sequences is an active area of research in computer vision. Most of the previous approaches on activity recognition focus on recognition from a single view and ignore the issue of view invariance, and they deal with recognizing a single activity. There are only few published algorithms for segmenting and recognizing complex activities that are composed of more than one activity. In this paper, we present a view invariant human activity recognition approach that uses both motion and shape information. An augmented vector of both optical flow features as well as eigen shape features is used to represent motion and shape of the body in the region of interest in each frame of the sequence. Each activity is represented by a set of hidden Markov models, where each model represents the activity from a different viewing direction, to realize the view invariance. Also, we present a voting-based approach to automatically and effectively segment and recognize complex activities. Experiments on two sets of video clips of different activities show that our method is effective.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 it