Continuous Human Action Recognition by Multiple-Object-Detection-Based FMCW Radar
Why this work is in the frame
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Bibliographic record
Abstract
A continuous human activity recognition method based on the multiobject recognition (MOR) method, the constructed lightweight network (LNet), and the proposed one-dimensional bounding loss (ODBL) function, the MOR LNet ODBL (MOR-LNOD) method, is proposed. The method is validated using continuous action sequences involving nine participants and eight different actions. We interpret each action in the sequence as a single target and utilize a multiobject detection method for accurate single-action region selection, followed by recognition and classification. Based on the results of the study, the MOR-LNOD method is 96.5% accurate on average, which is an improvement of about 20% compared with previous methods based on recurrent neural networks. Compared to the ResNet 50 and MobileNet used in the traditional faster region-based convolutional neural network, the proposed network architecture has reduced the parameters by ten and two times, respectively. As compared to state of the art (SOTA) on the publicly available dataset, MOR-LNOD not only reduces the requirement of input data but also has a higher average accuracy than SOTA.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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