Object Detection and Pattern of Life Analysis from Remotely Piloted Aircraft System Acquired Full Motion Video
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».