Multimodal video and radar system for edge devices object detection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The use of multimodal systems brings great prospects in solving complex problems unsolvable by the usual unimodal approaches. Therefore, a multimodal video and radar system are proposed to design a multimodal machine-learning problem on edge devices. The system’s architecture uses Docker containers to capture knowledge under models and processes, allowing the system to be easily managed. Furthermore, the importance of object detection is enhanced in the proposed system, as the identification and localization of objects in different data modalities are critical components of several multimodal machine-learning tasks. Hence, it is presented as an overall description of the architecture and a discussion of the data pipeline of this system. It is approached by the challenge of data alignment using homographic transformations with video camera and radar data, as well as using the system calibration to reach the data fusion and consequent predictions. It highlights the advantages of multimodal systems in dealing with complex and dynamic environments and provides a general approach to multimodal machine-learning problems on edge devices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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