FastTuner: Fast Resolution and Model Tuning for Multi-Object Tracking in Edge Video Analytics
Bibliographic record
Abstract
Multi-object tracking (MOT) is the “killer app” of edge video analytics. Deploying MOT pipelines for live video analytics poses a significant system challenge due to their computation-intensive nature. In this paper, we propose FastTuner, a model-agnostic framework that aims to accelerate MOT pipelines by adapting frame resolutions and backbone models. Unlike prior works that utilize a separate and time-consuming online profiling procedure to identify the optimal configuration, FastTuner incorporates multi-task learning to perform configuration selection and object tracking through a shared model. Multi-resolution training is employed to further improve the tracking accuracy across different resolutions. Furthermore, two workload placement schemes are designed for the practical deployment of FastTuner in edge video analytics systems. Extensive experiments demonstrate that FastTuner can achieve 1.1%–9.2% higher tracking accuracy and 2.5%–25.5% higher speed compared to the state-of-the-art methods, and accelerate end-to-end processing by 1.7%–22.5% in a real-world testbed consisting of an embedded device and an edge server.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".