Object Detection and Pattern of Life Analysis from Remotely Piloted Aircraft System Acquired Full Motion Video
Bibliographic record
Abstract
Remotely piloted aircraft systems (RPAS) have introduced a new ability to quickly deploy low-cost, fully or partially autonomous aerial sensor platforms which has created new intelligence, surveillance, and reconnaissance capabilities in various domains using cameras which are ubiquitous in most RPAS. Despite the utility of these aerial sensor systems, the full motion video (FMV) they acquire presents a big data challenge for operators as they generate large volumes of data that are impractical to analyze using current workflows due to excessive time requirements, computational resources, cost, or the availability of human analysts. Additionally, moving the camera rather than having a static network of stationary cameras, complicates the data processing steps required to generate valuable outputs. In order to address this big data challenge, various artificial intelligence (AI) based algorithms and data analytic workflows that can extract useful insights and knowledge from large amounts of complex and ambiguous FMV data streams from airborne sensors are developed and assessed. A data acquisition campaign was launched resulting in a dataset consisting of 33 flights recording approximately eight and a half hours of RPAS acquired FMV to assess the suite of AI-based algorithmic tools. Some of the tools useful for analyzing aerial FMV include object detection, and tracking namely to conduct pattern of life (POL) analysis for which aerial sensors mounted on RPAS are well suited for as they capture spatiotemporal information crucial to understanding the context of a scenario. Analysis and interpretation of the acquired dataset revealed that state of the art performance was achieved using the AI-based tools when the RPAS was deployed under an altitude of 30 m, at a velocity of under 7 m/s, and at pitch angles ranging from 25° to 65° while acquiring FMV at a resolution of 4.16 MP. The POL analysis conducted on two flights proved the two developed feature engineering based workflows to be robust behavioral anomaly detection tools for the staged pedestrian traffic and high value target assailant scenarios. The acquired data was also visualized in virtual reality within an immersive four-dimensional scene as a novel enhanced dissemination tool to aid in the POL interpretation and decision making. The acquisition, processing, analysis, and dissemination of the data from the 33 flights has indicated that RPAS acquired FMV combined with AI-based algorithmic tools could serve as an effective and reliable platform for creating and handling the big data for a variety of different applications such as peace support, public safety, and aerial monitoring to name a few.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".