Vision-based detection of non-cooperative UAVs using frame differencing and temporal filter

Briese, Christoph; Seel, Andreas; Andert, Franz

doi:10.1109/icuas.2018.8453372

Cited by 13 publications

(11 citation statements)

References 10 publications

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“…Based on the ground truth data, we evaluated the quality of the detection. In parallel, the results were compared with the values calculated by the method presented in [15]. Table II shows the results for the method from [15], the results with prediction from the convolutional network only, and in combination with the temporal tracking filter.…”

Section: Discussionmentioning

confidence: 99%

“…The propagation step of the Kalman filter provides an estimated object position even if the detection algorithm fails to find the object in one or more frames, see section V for results. Further details on the implementation of the temporal tracking filter and the integration in an image analysis pipeline can be found in [15].…”

Section: Temporal Filteringmentioning

confidence: 99%

“…6 e)f). In addition to the data presented in [15] the data set was extended by some relevant sequences. Four sequences have been determined, where the intruder entered the scene after about half the time elapsed (ca.…”

Section: B Image Sequencesmentioning

confidence: 99%

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Deep Learning with Semi-Synthetic Training Images for Detection of Non-Cooperative UAVs

Briese

Guenther

2019

2019 International Conference on Unmanned Aircraft Systems (ICUAS)

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This paper presents a method to generate a dataset for training a deep convolutional network to detect a non cooperative unmanned aerial vehicle in video data. Deep convolutional network have shown a great potential for tasks like object detection and have been continuously improved in the last years. Still, the amount of training data is large and their generation can be complex and time consuming, especially if the appearance of the detected object is not clearly specified. The concept presented here is to train a deep convolutional neural network just with a few two dimensional images of unmanned aerial vehicle to simplify the process of generating training data. Performance of the trained network is evaluated with data from real experimental flights and compared with hand-labeled ground truth data to validate the correctness. To cover situations when the classifier fails at the detection, the output is integrated in a image processing pipeline for object tracking in order to establish a continuous tracking.

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Section: Discussionmentioning

confidence: 99%

Section: Temporal Filteringmentioning

confidence: 99%

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Deep Learning with Semi-Synthetic Training Images for Detection of Non-Cooperative UAVs

Briese

Guenther

2019

2019 International Conference on Unmanned Aircraft Systems (ICUAS)

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“…Let consider the projection of the target over the image plane of a camera. In this case, it is assumed that some visual feature points can be extracted from the target by means of some available computer vision algorithms like [35][36][37][38] or [39].…”

Section: Camera Measurement Model For the Projection Of The Targetmentioning

confidence: 99%

Monocular Visual SLAM Based on a Cooperative UAV–Target System

Trujillo

Munguía

Urzua

et al. 2020

Sensors

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To obtain autonomy in applications that involve Unmanned Aerial Vehicles (UAVs), the capacity of self-location and perception of the operational environment is a fundamental requirement. To this effect, GPS represents the typical solution for determining the position of a UAV operating in outdoor and open environments. On the other hand, GPS cannot be a reliable solution for a different kind of environments like cluttered and indoor ones. In this scenario, a good alternative is represented by the monocular SLAM (Simultaneous Localization and Mapping) methods. A monocular SLAM system allows a UAV to operate in a priori unknown environment using an onboard camera to simultaneously build a map of its surroundings while at the same time locates itself respect to this map. So, given the problem of an aerial robot that must follow a free-moving cooperative target in a GPS denied environment, this work presents a monocular-based SLAM approach for cooperative UAV–Target systems that addresses the state estimation problem of (i) the UAV position and velocity, (ii) the target position and velocity, (iii) the landmarks positions (map). The proposed monocular SLAM system incorporates altitude measurements obtained from an altimeter. In this case, an observability analysis is carried out to show that the observability properties of the system are improved by incorporating altitude measurements. Furthermore, a novel technique to estimate the approximate depth of the new visual landmarks is proposed, which takes advantage of the cooperative target. Additionally, a control system is proposed for maintaining a stable flight formation of the UAV with respect to the target. In this case, the stability of control laws is proved using the Lyapunov theory. The experimental results obtained from real data as well as the results obtained from computer simulations show that the proposed scheme can provide good performance.

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“…Let consider the projection of the lead agent over the image plane of a camera. In this case, it is assumed that some visual feature points can be extracted from the lead agent by means of some available computer vision algorithm like [39,40,41,42] or [43].…”

Section: System Specificationmentioning

confidence: 99%

Visual-Based SLAM Configurations for Cooperative Multi-UAV Systems with a Lead Agent: An Observability-Based Approach

Trujillo

Munguía

Guerra

et al. 2018

Sensors

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In this work, the problem of the cooperative visual-based SLAM for the class of multi-UA systems that integrates a lead agent has been addressed. In these kinds of systems, a team of aerial robots flying in formation must follow a dynamic lead agent, which can be another aerial robot, vehicle or even a human. A fundamental problem that must be addressed for these kinds of systems has to do with the estimation of the states of the aerial robots as well as the state of the lead agent. In this work, the use of a cooperative visual-based SLAM approach is studied in order to solve the above problem. In this case, three different system configurations are proposed and investigated by means of an intensive nonlinear observability analysis. In addition, a high-level control scheme is proposed that allows to control the formation of the UAVs with respect to the lead agent. In this work, several theoretical results are obtained, together with an extensive set of computer simulations which are presented in order to numerically validate the proposal and to show that it can perform well under different circumstances (e.g., GPS-challenging environments). That is, the proposed method is able to operate robustly under many conditions providing a good position estimation of the aerial vehicles and the lead agent as well.

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Vision-based detection of non-cooperative UAVs using frame differencing and temporal filter

Cited by 13 publications

References 10 publications

Deep Learning with Semi-Synthetic Training Images for Detection of Non-Cooperative UAVs

Deep Learning with Semi-Synthetic Training Images for Detection of Non-Cooperative UAVs

Monocular Visual SLAM Based on a Cooperative UAV–Target System

Visual-Based SLAM Configurations for Cooperative Multi-UAV Systems with a Lead Agent: An Observability-Based Approach

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