Trajectory-based human activity recognition from videos
Bibliographic record
Abstract
Sparse representation is widely used by different human activity recognition methods. Although many sparse feature extraction algorithms have been proposed in the literature, most of them focused on low-level features. This paper proposes a new method using trajectories, as mid-level features, for human activity recognition. Even though the use of trajectories is not new in this field, their potential is yet to be fully attained. In this paper, inspired by previous works, we have proposed new trajectory extraction methods, which are very flexible. Then we have emphasized the difference between trajectories and traditional descriptors, and have shown the advantages of using trajectories for human activity recognition. The pros and cons of trajectories are demonstrated through proposed trajectory-based methods. We have used a simple shape descriptor and the standard bag of word algorithm for human activity classification. The results of these different algorithms have been compared. We have also compared our results with other popular existing methods based on low level extracted features. In particular, we have shown that using proposed sparse trajectories can produce similar or better results than using dense trajectories. Furthermore, the computational time has been reduced as we are dealing with fewer data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".