Coupled Observation Decomposed Hidden Markov Model for Multiperson Activity Recognition
Bibliographic record
Abstract
Multiperson activity recognition in videos is a challenging task, due to the complexity of interactions among multiple persons. In this paper, a new statistical model, named coupled observation decomposed hidden Markov model (CODHMM), is presented to model multiperson activities in videos. A human activity that involves multiple persons is analyzed in two levels: the individual level that describes each individual's motion details and the interaction level that expresses the shared information among multiple persons. The two levels are modeled by two hidden Markov chains that are interdependent and interact with each other. The observation in each chain at each time slice is decomposed into subobservations according to the number of features and the number of persons. For each activity to be recognized, a CODHMM is built and model parameters are learnt by a generalized expectation maximization (EM) algorithm. Given an input video that contains an unknown activity, maximum likelihood algorithms are developed to classify it into one of the learnt activity categories. Experimental results show that the CODHMM can successfully classify human activities involving multiple persons with high accuracy and low computations.
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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 | 0.000 | 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 | 0.001 | 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 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".