Visual Multiple Target Tracking From a Descending Aerial Platform

Lusk, Parker C.; Beard, Randal W.

doi:10.23919/acc.2018.8431915

Cited by 5 publications

(3 citation statements)

References 25 publications

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“…In our implementation, the tracking UAS and the handoff UAS both visually track the ground targets using the R-RANSAC-based visual multiple target tracking (VMTT) algorithm, originally presented in [12], and extended in [13], [14], [15], [16] to tracking from multi-rotor aircraft. In this paper we extend the algorithm to fixed-wing aircraft.…”

Section: Multiple Target Trackingmentioning

confidence: 99%

Robust Moving Target Handoff in GPS-Denied Environments

Beard¹,

Tolman²,

Morris³

et al. 2021

Preprint

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Unmanned aerial systems (UAS) are effective for surveillance and monitoring, but struggle with persistent, longterm tracking due to limited ﬂight time. Persistent tracking can be accomplished using multiple vehicles if one vehicle can effectively hand off the tracking information to another replacement vehicle. In this paper we propose a solution to the moving-target handoff problem in the absence of GPS. The proposed solution uses a nonlinear complimentary ﬁlter for self-pose estimation using only an IMU, a particle ﬁlter for relative pose estimation between UAS using a relative range measurement, visual target tracking using a gimballed camera when the target is close to the handoff UAS, and track correlation logic using Procrustes analysis to perform the ﬁnal target handoff between vehicles. We present extensive simulation results that demonstrates the effectiveness of our approach and perform Monte-Carlo simulations that indicate a 97% successful handoff rate using the proposed methods.

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Section: Multiple Target Trackingmentioning

confidence: 99%

Robust Moving Target Handoff in GPS-Denied Environments

Beard¹,

Tolman²,

Morris³

et al. 2021

Preprint

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show abstract

“…The contour information of the classified people could lead to an operator trusting the algorithm because the shape is consistent with the classification result. Another example mission is to confirm a landing zone is safe to land without people below [9]. Fig.…”

Section: Mask R-cnn Segmentation Approachmentioning

confidence: 99%

Towards Explainability of UAV-Based Convolutional Neural Networks for Object Classification

Dolph

Tran

Allen

2018

2018 Aviation Technology, Integration, and Operations Conference

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Establishing a basis for certification of autonomous systems using trust and trustworthiness is the focus of Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR), a new NASA Convergent Aeronautical Solutions (CAS) Project. One critical research element of ATTRACTOR is explainability of the decision-making across relevant subsystems of an autonomous system. The ability to explain why an autonomous system makes a decision is needed to establish a basis of trustworthiness to safely complete a mission. Convolutional Neural Networks (CNNs) are popular visual object classifiers that have achieved high levels of classification performances without clear insight into the mechanisms of the internal layers and features. To explore the explainability of the internal components of CNNs, we reviewed three feature visualization methods in a layer-by-layer approach using aviation related images as inputs. Our approach to this is to analyze the key components of a classification event in order to generate component labels for features of the classified image at different layers of depths. For example, an airplane has wings, engines, and landing gear. These could possibly be identified somewhere in the hidden layers from the classification and these descriptive labels could be provided to a human or machine teammate while conducting a shared mission and to engender trust. Each descriptive feature may also be decomposed to a combination of primitives such as shapes and lines. We expect that knowing the combination of shapes and parts that create a classification will enable trust in the system and insight into creating better structures for the CNN.

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“…Thus, to handle this problem, variants of the Kalman filter are used for tracking in UAV environments. In [5,30,32,45], Kalman filters were applied to a setting on the homography plane where the UAV ego-motion was removed. This method of applying Kalman filter on the homography plane is referred to as a homographic Kalman filter (HKF).…”

Section: Introductionmentioning

confidence: 99%

Motion-aware ensemble of three-mode trackers for unmanned aerial vehicles

Lee

Chang

Choi

et al. 2021

Machine Vision and Applications

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To tackle problems arising from unexpected camera motions in unmanned aerial vehicles (UAVs), we propose a three-mode ensemble tracker where each mode specializes in distinctive situations. The proposed ensemble tracker is composed of appearance-based tracking mode, homography-based tracking mode, and momentum-based tracking mode. The appearance-based tracking mode tracks a moving object well when the UAV is nearly stopped, whereas the homography-based tracking mode shows good tracking performance under smooth UAV or object motion. The momentum-based tracking mode copes with large or abrupt motion of either the UAV or the object. We evaluate the proposed tracking scheme on a widely-used UAV123 benchmark dataset. The proposed motion-aware ensemble shows a 5.3% improvement in average precision compared to the baseline correlation filter tracker, which effectively

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Visual Multiple Target Tracking From a Descending Aerial Platform

Cited by 5 publications

References 25 publications

Robust Moving Target Handoff in GPS-Denied Environments

Robust Moving Target Handoff in GPS-Denied Environments

Towards Explainability of UAV-Based Convolutional Neural Networks for Object Classification

Motion-aware ensemble of three-mode trackers for unmanned aerial vehicles

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