Aerial surveillance system with cognitive swarm drones

Gotzy, Marton; Hetenyi, Daniel; Blázovics, László

doi:10.1109/cyberi.2016.7438593

Cited by 7 publications

(4 citation statements)

References 15 publications

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“…Note that there is an increasing number of works proposing the use of UAVs in a semi-autonomous fashion to reduce operator's dependence, e.g., Yang et al (2015), and the use of this kind of system to perform surveillance, such as in Gotzy et al (2016) and Chakrabarty et al (2016). As the main contribution of this research work, human activity is recognized and monitored by a UAV through perceptual data.…”

Section: The Work Ofmentioning

confidence: 98%

Surveillance Architecture for Human Activity Recognition using Unmanned Aerial Vehicle

Pinto

Melo

Marcato

et al. 2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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There is an intensive growth in researches regarding surveillance and threat detection. Surveillance tasks often involve several actors with multiple interactions. Thus, modeling a complex activity becomes challenging. This work proposes an architecture comprised of low, middle, and high levels. The low-level recognizes characteristics, positioning of objects, and time of occurrences utilizing a camera and Unmanned Aerial Vehicle (UAV) sensors. The middle-level is responsible for structuring the information from the low-level using Deterministic Finite Automata (DFA). An expert system attached in the high-level module performs inference over the organized information to enables the system to have simple reasoning modules, assisting the operator decision. The architecture is embedded in a UAV to reduce the number of cameras and to reach difficult areas. The experiments showed that the proposed system updated the grammatical structure effectively given a sequence of information computed by the vision modules.

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Section: The Work Ofmentioning

confidence: 98%

Surveillance Architecture for Human Activity Recognition using Unmanned Aerial Vehicle

Pinto

Melo

Marcato

et al. 2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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“…Few works can be cited in the context of the learning process in autonomous systems [10], [11], [12], [13]. The system in [13] has a good ability to track but a low capacity to adapt and learn.…”

Section: Work Motivationmentioning

confidence: 99%

ARCog: An Aerial Robotics Cognitive Architecture

et al. 2020

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SUMMARY Efficient algorithm integration is a key issue in aerial robotics. However, only a few integration solutions rely on a cognitive approach. Cognitive approaches break down complex problems into independent units that may deal with progressively lower-level data interfaces, all the way down to sensors and actuators. A cognitive architecture defines information flow among units to produce emergent intelligent behavior. Despite the improvements in autonomous decision-making, several key issues remain open. One of these issues is the selection, coordination, and decision-making related to the several specialized tasks required for fulfilling mission objectives. This work addresses decision-making for the cognitive unmanned-aerial-vehicle architecture coined as ARCog. The proposed architecture lays the groundwork for the development of a software platform aligned with the requirements of the state-of-the-art technology in the field. The system is designed to provide high-level decision-making. Experiments prove that ARCog works correctly in its target scenario.

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“…Note that there is an increasing number of works proposing the use of UAVs in a semiautonomous fashion to reduce operator's dependence, e.g., Yang et al (2015), and the use of this kind of system to perform surveillance, such as in Gotzy et al (2016) and Chakrabarty et al (2016). As the main contribution of this research work, human activity is recognized and monitored by a UAV through perceptual data.…”

Section: Introductionmentioning

confidence: 98%