OAVS: Efficient Online Learning of Streaming Policies for Drone-sourced Live Video Analytics
Bibliographic record
Abstract
Drone-sourced live video analytics has extensive applications across diverse domains. Adaptive video streaming is a pivotal technique in these applications that targets at effectively delivering video content to servers under varying network conditions, enabling complex analytics afterward. However, our thorough data analysis reveals that conventional offline video streaming policies cannot effectively adapt to highly fluctuating drone network environments and dynamic changes in aerial view scenes. This results in suboptimal analytic performance and necessitates online adaptation for policy models. Yet, obtaining ground-truth analytics results directly from drones is infeasible due to their limited capacity. Furthermore, naively streaming original videos to the server for online adaption is greatly challenged by the scarce and dynamic networks, leading to decreased accuracy performance and escalated transmission cost if not properly designed. In this paper, we present OAVS, a novel online learning-enabled adaptive streaming framework for drone-sourced video analytics. To facilitate cost-effective online retraining, we design a hierarchical reinforcement learning approach in which the upper-level module intelligently determines the timing for online retraining, balancing machine-perceived quality of experience (QoE) improvement and transmission cost. Meanwhile, the lower-level module dynamically allocates bitrate to maximize machine-perceived QoE. Extensive experiments based on real-world drone video and aerial network datasets demonstrate that our proposed framework achieves a 17.7% mean accuracy increase, a 37.5% decrease in the mean failure rate of video uploading, and a 5.2% mean latency decrease compared to state-of-the-art solutions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".