Persistent Aerial Tracking system for UAVs

Mueller, Matthias; Sharma, Gopal; Smith, Neil; Ghanem, Bernard

doi:10.1109/iros.2016.7759253

Cited by 55 publications

(46 citation statements)

References 24 publications

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“…There are also some papers where the flight could have virtually infinite number of stages. This is the case in [19] discussing a tracking system. There, we can see three primary cruise stages: (1) UAV is directed manually to the center of the field, (2) UAV starts tracking the target, (3) upon the battery being close to run out, first UAV sends a signal to GCS and another UAV flies to replace it.…”

Section: Nomenclaturementioning

confidence: 99%

Multiple UAV Systems: A Survey

2020

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Nowadays, Unmanned Aerial Vehicles (UAVs) are used in many different applications. Using systems of multiple UAVs is the next obvious step in the process of applying this technology for variety of tasks. There are few research works that cover the applications of these systems and they are all highly specialized. The goal of this survey is to fill this gap by providing a generic review on different applications of multiple UAV systems that have been developed in recent years. We also present a nomenclature and architecture taxonomy for these systems. In the end, a discussion on current trends and challenges is provided.

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Section: Nomenclaturementioning

confidence: 99%

Multiple UAV Systems: A Survey

2020

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“…Here is the deduction from (2) to (3). The summation term in the first norm in (2) can be simplified as follows:…”

Section: Appendixmentioning

confidence: 99%

“…Visual object tracking plays a crucial role in unmanned aerial vehicle (UAV) applications such as obstacle avoidance [1], wild-life monitoring [2] and object following [3] (e.g. humans, cars, boats, etc.).…”

Section: Introductionmentioning

confidence: 99%

Boundary Effect-Aware Visual Tracking for UAV with Online Enhanced Background Learning and Multi-Frame Consensus Verification

Huang

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Due to implicitly introduced periodic shifting of limited searching area, visual object tracking using correlation filters often has to confront undesired boundary effect. As boundary effect severely degrade the quality of object model, it has made it a challenging task for unmanned aerial vehicles (UAV) to perform robust and accurate object following. Traditional hand-crafted features are also not precise and robust enough to describe the object in the viewing point of UAV. In this work, a novel tracker with online enhanced background learning is specifically proposed to tackle boundary effects. Real background samples are densely extracted to learn as well as update correlation filters. Spatial penalization is introduced to offset the noise introduced by exceedingly more background information so that a more accurate appearance model can be established. Meanwhile, convolutional features are extracted to provide a more comprehensive representation of the object. In order to mitigate changes of objects' appearances, multi-frame technique is applied to learn an ideal response map and verify the generated one in each frame. Exhaustive experiments were conducted on 100 challenging UAV image sequences and the proposed tracker has achieved state-of-the-art performance.

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“…Nowadays, the camera became the default onboard UAV sensor, it's a cheap, passive, and weightless sensor which can provide great information about the UAV's surroundings. As a result, diverse researches have been built to investigate the benefits of using a camera sensor in different applications, for instance, UAV target tracking [8], [9], aerial surveillance [10], [11], UAV path planning [12], fire observation [13], [14], and HMI [15], [16]. These types of researches used image information for important and practical applications in our daily life.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Human-UAV Interaction: New Dataset and Two Novel Gesture-Based Interacting Systems

et al. 2020

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Two novel gesture-based Human-UAV Interaction (HUI) systems are proposed to launch and control a UAV in real-time utilizing a monocular camera and a ground computer. The first proposal is an endto-end static Gesture-Based Interaction (GBI) system based on classifying the interacted user poses while discarding the gesture interpreting component to boost the system performance up to 99 % with a speed of 28 fps. On the other hand, the second proposal is a dynamic one that adopts a simple model to detect three parts (face and two hands) of the interacted person and tracks them for a certain number of frames till a specific dynamic gesture is recognized. The proposed dynamic method is efficient, decreases the complexity, and speeds the interaction up to 27 fps comparing with the recent multimodel ones. Its backbone is a simplified Tiny-You Only Look Once (YOLO) network saves the resources and speeds the detection process up to 120 fps. Moreover, a comprehensive new gestures dataset was established to facilitate the learning process and aid the research work. A comparative study is carried out to show the performance and efficiency of the proposed dynamic HUI system in terms of detection accuracy and speed with the baseline detector on the human gesture public dataset. Finally, a non-expert volunteer examines the proposed HUIs by launching and driving a Bebop 2 micro UAV through a set of real flights. INDEX TERMS Gesture dataset, gesture recognition, human-machine interaction, object tracking.

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Persistent Aerial Tracking system for UAVs

Cited by 55 publications

References 24 publications

Multiple UAV Systems: A Survey

Multiple UAV Systems: A Survey

Boundary Effect-Aware Visual Tracking for UAV with Online Enhanced Background Learning and Multi-Frame Consensus Verification

Real-Time Human-UAV Interaction: New Dataset and Two Novel Gesture-Based Interacting Systems

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