Human-tracking system using quadrotors and multiple environmental cameras for face-tracking application

Srisamosorn, Veerachart; Kuwahara, Noriaki; Yamashita, Atsushi; Ogata, Taiki; Ota, Jun

doi:10.1177/1729881417727357

Cited by 12 publications

(6 citation statements)

References 24 publications

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“…The high efficiency of the KP network is also due to its singleepoch learning style, which allows the learning to be much faster than that from the BP based methods that need hundreds or even thousands of epochs. Such a fast learning pace is particularly desired in real-time applications such as object tracking (e.g., face tracking [21]). The learning strategy in each layer, illustrated by ( 4) and ( 7), involves neither gradient nor BP.…”

Section: B Kernel Projection and Analytic Learning Of Weightsmentioning

confidence: 99%

Training Multilayer Neural Networks Analytically Using Kernel Projection

Zhuang

Lin

Toh

2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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This paper proposes a kernel projection (KP) neural network that analytically determines its network parameters. The proposed network is composed of cascaded modules of 2-layer sub-networks. A technique which encodes the label information into each module has been introduced to enable a locally supervised learning. Such a supervised learning in the 2-layer module begins with a kernel projection in the first layer and determines its parameters analytically via solving a least squares problem in the second layer. We show that the analytic nature of the proposed network allows a learning process significantly faster than that of the traditional backpropagation method as it only needs to visit the dataset once. Experiments of classification tasks on various datasets are carried out, showing comparable or better results compared with several competing methods.Index Terms-analytic learning, least squares, kernel project.

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Section: B Kernel Projection and Analytic Learning Of Weightsmentioning

confidence: 99%

Training Multilayer Neural Networks Analytically Using Kernel Projection

Zhuang

Lin

Toh

2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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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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Section: Camera Measurement Model For the Targetmentioning

confidence: 99%

Cooperative Visual-SLAM System for UAV-Based Target Tracking in GPS-Denied Environments: A Target-Centric Approach

et al. 2020

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Autonomous tracking of dynamic targets by the use of Unmanned Aerial Vehicles (UAVs) is a challenging problem that has practical applications in many scenarios. In this context, a fundamental aspect that must be addressed has to do with the position estimation of aerial robots and a target to control the flight formation. For non-cooperative targets, their position must be estimated using the on-board sensors. Moreover, for estimating the position of UAVs, global position information may not always be available (GPS-denied environments). This work presents a cooperative visual-based SLAM (Simultaneous Localization and Mapping) system that allows a team of aerial robots to autonomously follow a non-cooperative target moving freely in a GPS-denied environment. One of the contributions of this work is to propose and investigate the use of a target-centric SLAM configuration to solve the estimation problem that differs from the well-known World-centric and Robot-centric SLAM configurations. In this sense, the proposed approach is supported by theoretical results obtained from an extensive nonlinear observability analysis. Additionally, a control system is proposed for maintaining a stable UAV flight formation with respect to the target as well. In this case, the stability of control laws is proved using the Lyapunov theory. Employing an extensive set of computer simulations, the proposed system demonstrated potentially to outperform other related approaches.

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Human-tracking system using quadrotors and multiple environmental cameras for face-tracking application

Cited by 12 publications

References 24 publications

Training Multilayer Neural Networks Analytically Using Kernel Projection

Training Multilayer Neural Networks Analytically Using Kernel Projection

Monocular Visual SLAM Based on a Cooperative UAV–Target System

Cooperative Visual-SLAM System for UAV-Based Target Tracking in GPS-Denied Environments: A Target-Centric Approach

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