A system for the generation of synthetic Wide Area Aerial surveillance imagery

Griffith, Elias J.; Mishra, Chinmay; Ralph, Jason F.; Maskell, Simon

doi:10.1016/j.simpat.2018.03.003

Cited by 9 publications

(6 citation statements)

References 32 publications

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“…Virtual flight simulators can come in handy to render photo realistic videos of wide areas. Griffith et al [29] proposed a system for the generation of synthetic wide area aerial surveillance imagery. The authors simulated the traffic in an urban environment using Matlab and a traffic simulator (SUMO).…”

Section: Simulationmentioning

confidence: 99%

Survey on Videos Data Augmentation for Deep Learning Models

Cauli

Recupero

2022

Future Internet

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In most Computer Vision applications, Deep Learning models achieve state-of-the-art performances. One drawback of Deep Learning is the large amount of data needed to train the models. Unfortunately, in many applications, data are difficult or expensive to collect. Data augmentation can alleviate the problem, generating new data from a smaller initial dataset. Geometric and color space image augmentation methods can increase accuracy of Deep Learning models but are often not enough. More advanced solutions are Domain Randomization methods or the use of simulation to artificially generate the missing data. Data augmentation algorithms are usually specifically designed for single images. Most recently, Deep Learning models have been applied to the analysis of video sequences. The aim of this paper is to perform an exhaustive study of the novel techniques of video data augmentation for Deep Learning models and to point out the future directions of the research on this topic.

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Section: Simulationmentioning

confidence: 99%

Survey on Videos Data Augmentation for Deep Learning Models

Cauli

Recupero

2022

Future Internet

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“…The programming code is written in the m-file. The synthetic data approach permits an exploration of unusual configurations, unique system behavior, and specific parameter [17]. The synthetic data methodology has been widely applied in the robotics field.…”

Section: Find X X Xδ Based On the Fitness Value Yesmentioning

confidence: 99%

“…The synthetic data methodology has been widely applied in the robotics field. Using MATLAB software, the synthetic data has been integrated into the comprehensive systems in solving adaptive trajectory planning [18], Unmanned Aerial Vehicle (UAV) coordination [19], Wide Area Aerial surveillance [17], and route choice problem in transportation science [20]…”

Section: Find X X Xδ Based On the Fitness Value Yesmentioning

confidence: 99%

Untitled

2021

Int.J.Artif.Intell.Robot.

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I. INTRODUCTIONNowadays, sustainability becomes a general requirement in engineering and sciences, including in path planning technologies. Path planning is very important for an object, which needs to generate motion, such as a vehicle robot, an arm robot manipulator, a missile, a ship, a submarine, and an aircraft. It is a process to design a feasible path that becomes a trace for vehicles or robots.One of the advantages of using a Bezier curve for path planning is its adaptability to change the control points. It has been widely used as a manufacturing path in additive manufacturing [1,2,3], vehicle route path of an aircraft [4], and vessel path motion [5]. The goal of sustainability in the manufacturing system is still in the way of the process. In the future, additive manufacturing will be developed into sustainable manufacturing via path planning technologies [1]. Thus, research on the path planning methodologies that address a sustainability goal is highly important.Sustainable manufacturing refers to applying an approach for manufacturing that has minimal harm to environments and mitigated bad impacts on human health and nature [6]. For the robots and vehicle systems, which commonly operate in obstacle environments, it is necessary to achieve the safety goal by generating a collision-free path. Collision avoidance strategies become an important issue in path planning. The concept of collision avoidance dynamic critical area for vessel path planning was introduced in [5]. Collision avoidance for human-robot interactions in adaptive manufacturing was presented in [3]. Saeed et al.[7] proposed a boundary node method to solve mobile robot path planning in static obstacle environments. Kala et al. [8] applied multi-neuron heuristic search to solve the path planning in the static obstacle environment.The GA as one of the evolutionary methods has been applied to solve the point-to-point path planning. Linquan et al.[9] proposed to use GA for path planning of robot soccer systems in a dynamic environment. Choi et al. [10] presented two-path planning algorithms based on Bezier curves for autonomous vehicles with waypoints and corridor constraints. Lil et al. 11] assumed the existence of an obstacle-avoiding polyline path and replaced the polyline path with a G 2 cubic spline curve to avoid the obstacles.

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“…A brief overview of the image and ground truth generation method is described here (see Figure 5), a more detailed discussion is presented in [19].…”

Section: A Datasetmentioning

confidence: 99%

“…This generates an image of 1.8-Gigapixels (or 5-Gigabytes of uncompressed RGB imagery per frame) capturing a circular area of approximately 6km diameter (at a 6km altitude). Equations describing the configuration of the subcamera array can be found in [19].…”

Section: A Datasetmentioning

confidence: 99%

Convolutional Neural Networks for Aerial Vehicle Detection and Recognition

Soleimani

Nasrabadi

Griffith

et al. 2018

NAECON 2018 - IEEE National Aerospace and Electronics Conference

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This paper investigates the problem of aerial vehicle recognition using a text-guided deep convolutional neural network classifier. The network receives an aerial image and a desired class, and makes a yes or no output by matching the image and the textual description of the desired class. We train and test our model on a synthetic aerial dataset and our desired classes consist of the combination of the class types and colors of the vehicles. This strategy helps when considering more classes in testing than in training. 1

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A system for the generation of synthetic Wide Area Aerial surveillance imagery

Cited by 9 publications

References 32 publications

Survey on Videos Data Augmentation for Deep Learning Models

Survey on Videos Data Augmentation for Deep Learning Models

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Convolutional Neural Networks for Aerial Vehicle Detection and Recognition

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