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A kernel particle filter multi-object tracking using gabor-based region covariance matrices

A kernel particle filter multi-object tracking using gabor-b...

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IEEE International Conference on Image Processing

作者： Hélio Palaio Jorge Batista Institute of Systems and Robotics Department of Electrical Engineering and Computers Faculty of Science and Technology University of Coimbra Portugal

This paper presents an approach to label and track multiple objects through both temporally and spatially significant occlusions. To this end, tracking is performed at both the region level and the object level. At the region level, a kernel based particle filter method is used to search for optimal region tracks which limits the scope of object trajectories. At the object level, each object is located based on adaptive appearance models, spatial distributions and inter-occlusion relationships. Region covariance matrices are used to model objects appearance. We analyzed the advantages of using Gabor functions as features and embedded them in the RCMs to get a more accurate descriptor. The proposed architecture is capable of tracking multiple objects even in the presence of periods of full occlusions. Results from experiments with real video data show the effectiveness of the approach hereby proposed.

关键词： Kernel Particle filters Particle tracking Covariance matrix Target tracking Filtering Object detection Image sequences Layout Chromium

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Sensor Selection for Hypothesis Testing in Wireless Sensor Networks: a Kullback-Leibler Based Approach

Sensor Selection for Hypothesis Testing in Wireless Sensor N...

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Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference

作者： Dragana Bajovi Bruno Sinopoli Joo Xavier Institute for Systems and Robotics (ISR) Instituto Superior Técnico (IST) Department of Electrical and Computer Engineering Carnegie Mellon University

We consider the problem of selecting a subset of p out of n sensors for the purpose of event detection,in a wireless sensor network（WSN）.Occurrence of the event of interest is modeled as a binary Gaussian hypothesis *** this case sensor selection consists of finding,among all（~n） combinations,the one maximizing the Kullback-Leibler（KL） distance between the induced p-dimensional distributions under the two *** exhaustive search is impractical if n and p are large,as the resulting optimization problem is *** propose a suboptimal approach with computational complexity of order■（np）.This consists of relaxing the 0/1 constraint on the entries of the selection matrices to let the optimization problem search over the set of Stiefel *** finding the Stiefel matrix is a nonconvex problem,we provide an algorithm that is guaranteed to produce a global optimum for p = 1,through a series of judicious problem *** case p > 1 is tackled by an incremental,greedy *** obtained Stiefel matrix is then used to determine the sensor selection matrix which best approximates its range *** simulations are used to assess near optimality of the proposed *** also show how the proposed approach performs better than exhaustive searches once an upper bound on the computation time is set.

关键词： Sensor Selection for Hypothesis Testing in Wireless Sensor Networks a Kullback-Leibler Based Approach

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Accelerometer-based control of an industrial robotic arm

Accelerometer-based control of an industrial robotic arm

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Pedro Neto J. Norberto Pires A. Paulo Moreira Industrial Robotics Laboratory-Mechanical Engineering Department POLO II University of Coimbra Coimbra Portugal Institute of Systems and Robotics (ISR)-Department of Electrical and Computer Engineering University of Porta Porto Portugal

Most of industrial robots are still programmed using the typical teaching process, through the use of the robot teach pendant. In this paper is proposed an accelerometer-based system to control an industrial robot using two low-cost and small 3-axis wireless accelerometers. These accelerometers are attached to the human arms, capturing its behavior (gestures and postures). An Artificial Neural Network (ANN) trained with a back-propagation algorithm was used to recognize arm gestures and postures, which then will be used as input in the control of the robot. The aim is that the robot starts the movement almost at the same time as the user starts to perform a gesture or posture (low response time). The results show that the system allows the control of an industrial robot in an intuitive way. However, the achieved recognition rate of gestures and postures (92%) should be improved in future, keeping the compromise with the system response time (160 milliseconds). Finally, the results of some tests performed with an industrial robot are presented and discussed.

关键词： Industrial control Service robots electrical equipment industry Defense industry Educational robots Robot control Control systems Accelerometers Artificial neural networks Delay

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Cooperative Autonomous Marine Vehicle motion control in the scope of the EU GREX Project: Theory and Practice

Cooperative Autonomous Marine Vehicle motion control in the ...

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OCEANS - Europe

作者： A. Aguiary J. Almeiday M. Bayaty B. Cardeiray R. Cunhay A. Hauslery P. Mauryay A. Oliveiray A. Pascoaly A. Pereira M. Rufinoy L. Sebastiaoy C. Silvestrey F. Vanniy Dept. Electrical Engineering and Institute for Systems and Robotics (ISR) Instituto Superior Tecnico (IST) Lisbon Portugal Department Computer Science University슠of슠Southern California USA Dept. Computer Science Univ. Southern California (USC) USA

This paper describes the core of the research work done by the isr/IST team on cooperative autonomous marine vehicle (AMV) motion control in the scope of the EU Project GREX - coordination and control of cooperating heterogeneous unmanned systems in uncertain environments. The first part of the paper affords the reader a concise introduction to the general problem of cooperative motion control of fleets of AMVs by highlighting illustrative mission scenarios developed collectively by the GREX partners and summarizing the main challenges that they pose to system engineers. This is followed by the description of a general architecture for cooperative autonomous marine vehicle control in the presence of time-varying communication topologies and communication losses that is rooted in a solid control-theoretic framework. The results of simulations with the NetMar SyS - networked marine systems simulator - of isr/IST are presented and show the efficacy of the algorithms developed for cooperative motion control. The last part of the paper focuses on practical issues and describes the results of tests at sea in the Azores, in the Summer of 2008. The paper concludes with a critical review of the work done and a discussion of theoretical and practical implementation issues that warrant further research and development effort.

关键词： Marine vehicles Motion control Communication system control Control systems Automotive engineering systems engineering and theory Network topology Solids Testing Research and development

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Robot navigation and manipulation based on a predictive associative memory

Robot navigation and manipulation based on a predictive asso...

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IEEE International Conference on Development and Learning, ICDL

作者： Sascha Jockel Mateus Mendes Jianwei Zhang A. Paulo Coimbra Manuel Crisostomo CINACS International Research Training Group Technical Aspects of Multimodal Systems (TAMS) Department of Informatics University of Hamburg Hamburg Germany ISR-Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Portugal Escola Superior de Tecnologia e Gestäo de Oliveira do Hospital (ESTGOH) Instituto Politécnico de Coimbra Coimbra Portugal

Proposed in the 1980s, the sparse distributed memory (SDM) is a model of an associative memory based on the properties of a high dimensional binary space. This model has received some attention from researchers of different areas and has been improved over time. However, a few problems have to be solved when using it in practice, due to the non-randomness characteristics of the actual data. We tested an SDM using different forms of encoding the information, and in two different domains: robot navigation and manipulation. Our results show that the performance of the SDM in the two domains is affected by the way the information is actually encoded, and may be improved by some small changes in the model.

关键词： Robots Navigation Associative memory Neurons Testing Encoding Artificial intelligence Costs Computer architecture Degradation

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A General Framework for Multiple Vehicle Time-Coordinated Path Following Control

A General Framework for Multiple Vehicle Time-Coordinated Pa...

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2009 American Control Conference (ACC 2009)

作者： Reza Ghabcheloo Isaac Kaminer A. Pedro Aguiar Antonio Pascoal Department of Inteligent Hydraulics and Automation Tampere Univ of Technology P.O.Box 589 33101 Tampere Finland Department of Mechanical and Astronautical Engineering Naval Postgraduate School Monterey CA 93943 USA Dept. Electrical Engineering and Computers and the Institute for Systems and Robotics Instituto Superior Tecnico Av. Rovisco Pais 1 1049-001 Lisboa Portugal

ISBN: (纸本)9781424445233

This paper describes a general framework for the study of multiple vehicle, time-coordinated path following (TC-PF) control problems. An example is the situation where a group of vehicles is tasked to maneuver and arrive at pre-assigned final positions at the same time in a collision-free manner, while reducing some optimality criterion. The time of arrival is not fixed a priori, and the vehicles must negotiate their speeds along the spatial paths that they follow in order to arrive simultaneously and avoid collision. The general framework adopted leads to integrated solutions to TC-PF problems that unfold in three steps: 1) Generation of Deconflicted Trajectories for a group of vehicles, 2) Path Following for each vehicle along its assigned path, and 3) Coordination of the relative motion of the vehicles along their paths, so as to guarantee deconfliction and meet desired temporal constraints such as equal times of arrival. The last step is accomplished by varying the speed of each vehicle about the nominal speed profile computed in step 1, based on the exchange of information with its neighbors. The paper formulates the problem mathematically, offers a general framework for its solution, and illustrates the efficacy of the proposed methodology in simulation with dynamic models of Autonomous Underwater Vehicles (AUVs).

关键词： Marine vehicles Remotely operated vehicles robotics and automation Computational modeling Vehicle dynamics Mathematical model Underwater vehicles Scattering Oceans Sea surface

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Robotic wheelchair control considering user comfort - Modeling and experimental evaluation

Robotic wheelchair control considering user comfort - Modeli...

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ICINCO 2008 - 5th International Conference on Informatics in Control, Automation and robotics

作者： Solea, Razvan Nunes, Urbano ISR - Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra 3030-290 Coimbra Portugal

ISBN: (纸本)9789898111319

This paper analyzes the comfort of wheelchair users when a sliding-mode trajectory-tracking controller is used. The transmission of the horizontal (fore-and-aft) vibration to the head-neck complex (HNC) in the seated human body may cause unacceptable discomfort and motion sickness. A double-inverted pendulum model with two degrees of freedom is considered as a model for the HNC. The user comfort is examined not only in the time domain (using the fourth power vibration dose value), but also in the frequency domain (using the cross-spectral density method). For measuring the acceleration of the wheelchair, along the trajectory, an inertial measurement unit was used.

关键词： Sliding mode control

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Improving the generalization properties of neural networks: An application to vehicle detection

Improving the generalization properties of neural networks: ...

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11th International IEEE Conference on Intelligent Transportation systems, ITSC 2008

作者： Ludwig Jr., Oswaldo Nunes, Urbano ISR-Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Polo II 3030-290 Coimbra Portugal

ISBN: (纸本)9781424421121

In this paper a Multilayer Feedforward Neural Network based approach for vehicle detection is proposed. The main idea is to use such network to perform both feature extraction and classification. This simplicity enables real time applications. In order to achieve such capabilities, the network is trained by a new algorithm, proposed in this paper, named Minimization of Inter- Class Interference (MCI). Such algorithm aims to create a hidden space (i.e. feature space) where the patterns have a desirable statistical distribution. Regarding the neural architecture, the linear output layer is replaced by the Mahalanobis kernel, in order to improve generalization. Experiments are performed by means of a dataset that includes two standard datasets from Caltech car rear. Finally, disturbed images are used, in order to evaluate the robustness of the neural-network based vehicle detection. The proposed method reveals low miss rate, low false alarm rate and high area under ROC curve. In Matlab environment, the algorithm spends only 3.280e-4 seconds per image. These facts encourage this research line. © 2008 IEEE.

关键词： Feedforward neural networks

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Robot navigation using a sparse distributed memory

Robot navigation using a sparse distributed memory

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IEEE International Conference on robotics and Automation (ICRA)

作者： Mateus Mendes Manuel Crisostomo A. Paulo Coimbra Department of Electrical and Computer Engineering ESTGOH IPC Coimbra Institute of sustems and robotics University of Coimbra Portugal ISR-Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Portugal Department of Electrical and Computer Engineering ISR-Institute of Systems and Robotics University of Coimbra Portugal

Despite all the progress that has been made in robotics and Artificial Intelligence, traditional approaches seem unsuitable to build truly intelligent robots, exhibiting human-like behaviours. Many authors agree that the source of intelligence is, to a great extent, the use of a huge memory, where sequences of events that guide our later behaviour are stored. Inspired by that idea, our approach is to navigate a robot using sequences of images stored in a Sparse Distributed Memory-a kind of associative memory based on the properties of high dimensional binary spaces, which, in theory, exhibits some human-like behaviours. The robot showed good ability to correctly follow most of the sequences learnt, with small errors and good immunity to the kidnapped robot problem.

关键词： Navigation Intelligent robots Humanoid robots Artificial intelligence robotics and automation Orbital robotics Humans Image retrieval Brain modeling Predictive models

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Single view head pose estimation

Single view head pose estimation

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IEEE International Conference on Image Processing

作者： Pedro Martins Jorge Batista Institute for Systems and Robotics Department of Electrical Engineering and Computers University of Coimbra Portugal

This work addresses the problem of human head pose estimation from single view images. 3D rigid head pose is estimated combining Active Appearance Models(AAM) with Pose from Orthography and Scaling with ITerations(POSIT). AAM shape landmarks are tracked over time and used in POSIT for pose estimation. A statistical anthropometric 3D model is used as reference. Several experiences were performed comparing our approach with a planar ground truth. It was achieved average standard deviations about 2 degrees in orientation and 1 centimeter in distance.

关键词： Magnetic heads Active appearance model Humans Biological system modeling Principal component analysis Vectors Shape control Robots Computer applications Computer interfaces

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