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作者： Feng, Ruoyu (TU Delft Electrical Engineering, Mathematics and Computer Science) Technische Universiteit Delft

学位级别：Delft University of Technology

Automotive radar is widely used for driving safety support and it is a key element of future autonomous vehicles. Radar sensors have the property that the performance is not to be affected by low vision conditions compared with camera sensors or laser based radar sensors, which makes it crucial for the autonomous driving *** radar system utilizes millimeter-wave band to detect the range, velocity and direction of arrival (DOA). Commercial 24 GHz and 77GHz radar have been well developed in vehicle applications at present, and the future trend is 79 GHz solution with wider 4 GHz bandwidth, which has been defined by the European Commission in 2004 as the frequency allocation for automotive shortrange radar systems. The main modulation method of automotive radar is Frequency-Modulated Continous-Wave(FMCW), which holds advantages including high-resolution distance measurement, quick updating and lower sampling frequency required on hardware.%good functions in the various weather *** resolution and accuracy of DOA estimation depend on the number of used antennas and their physical size. To improve the performance advanced algorithms and antenna configurations can be used. So-called super-resolution techniques can improve the estimation performance but suffer when few measurements are available and the number of targets is unknown, and the performance will also be degraded by correlated *** is an established technique for radars for precision angle estimation. It enables DOA realized in only one pulse and requires quite less computation complexity. Conventional monopulse with one transmitter and two receivers and phased array monopulse have been well developed especially in tracking radar system. MIMO-monopulse has been studied in some literature, but most of them utilize distributed MIMO. The research and application of colocated MIMO-monopulse are still in progress. In FMCW automotive radar application, clutter may have a strong effe

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A grid-based Bayesian approach to robust visual tracking

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DIGITAL SIGNAL PROCESSING 2012年第1期22卷 54-65页

作者： Liu, Xinmin Lin, Zongli Acton, Scott T. Univ Virginia Charles L Brown Dept Elect & Comp Engn Charlottesville VA 22904 USA

Visual tracking encompasses a wide range of applications in surveillance, medicine and the military arena. There are however roadblocks that hinder exploiting the full capacity of the tracking technology. Depending on specific applications, these roadblocks may include computational complexity, accuracy and robustness of the tracking algorithms. In the paper, we present a grid-based algorithm for tracking that drastically outperforms the existing algorithms in terms of computational efficiency, accuracy and robustness. Furthermore, by judiciously incorporating feature representation, sample generation and sample weighting, the grid-based approach accommodates contrast change, jitter, target deformation and occlusion. tracking performance of the proposed grid-based algorithm is compared with two recent algorithms, the gradient vector flow snake tracker and the Monte Carlo tracker, in the context of leukocyte (white blood cell) tracking and UAV-based tracking. This comparison indicates that the proposed tracking algorithm is approximately 100 times faster, and at the same time, is significantly more accurate and more robust, thus enabling real-time robust tracking. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Visual tracking Bayes' law Kalman filter Real-time Occlusion Multiple object tracking

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Trajectory planning for human host tracking and following of slave mobile robot on service-related tasks

Trajectory planning for human host tracking and following of...

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2011 ieeE International Conference on Robotics and Biomimetics, ROBIO 2011

作者： Chen, Chin-Lung Chou, Chih-Chung Lian, Feng-Li Department of Electrical Engineering National Taiwan University Taipei Taiwan

ISBN: (纸本)9781457721373

People tracking and following has become an increasing popular topic in recent years. The ability of robots to track the people in the surroundings is essential to many real life applications such as museum guidance, office or library assistance. Another aspect of human-robot interaction is the robot's ability to follow a human target. There are various scenarios where instructions, such as holding books in a library or carrying groceries at a store, are given to the robot when following the target host. Several references that provide more details on this video paper contribution can be found in [1: Chen et al. 2011] and [2: Chen et al. 2011]. The first paper focuses on detecting and tracking moving target in a dynamic environment. The second paper describes the proposed method "Trajectory Optimization" in great length and provides details of performance evaluation regarding robot maneuverability. Some of the important related work includes an architecture of moving object tracking similar to the one in our system being introduced in [3: Wang et al. 2007]. In the DATMO system, the widely used concept of occupancy grid map is adopted to distinguish static and dynamic objects which is similar to the work of [4: Wolf & Sukhatme 2004] Another key aspect of this video paper is regarding motion planning, there are many successful works such as [5: Fox et al. 1997], [6: Ulrich & Borenstein 2000], [7: Minguez & Montano 2004], [8: Seder & Petrovi'c 2007] of auto driving in static environments. However, those methods are designed to reach a fixed goal and assume that the environment and robot states are fully observable. Applying traditional obstacle avoidance algorithms on the target following task directly can fail easily because a moving target can change its speed and moving direction at anytime and the target can be occluded by obstacles. In our work, we propose a motion planner for moving target following. The planner uses an extension of dynamic window approach propos

关键词： Trajectories

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Computer Vision Onboard UAVs for Civilian Tasks

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2009年第1-3期54卷 105-135页

作者： Campoy, Pascual Correa, Juan F. Mondragon, Ivan Martinez, Carol Olivares, Miguel Mejias, Luis Artieda, Jorge Univ Politecn Madrid Comp Vis Grp E-28006 Madrid Spain Queensland Univ Technol ARCAA Sch Engn Syst Brisbane Qld 4000 Australia

Computer vision is much more than a technique to sense and recover environmental information from an UAV. It should play a main role regarding UAVs' functionality because of the big amount of information that can be extracted, its possible uses and applications, and its natural connection to human driven tasks, taking into account that vision is our main interface to world understanding. Our current research's focus lays on the development of techniques that allow UAVs to maneuver in spaces using visual information as their main input source. This task involves the creation of techniques that allow an UAV to maneuver towards features of interest whenever a GPS signal is not reliable or sufficient, e. g. when signal dropouts occur (which usually happens in urban areas, when flying through terrestrial urban canyons or when operating on remote planetary bodies), or when tracking or inspecting visual targets-including moving ones-without knowing their exact UMT coordinates. This paper also investigates visual servoing control techniques that use velocity and position of suitable image features to compute the references for flight control. This paper aims to give a global view of the main aspects related to the research field of computer vision for UAVs, clustered in four main active research lines: visual servoing and control, stereo-based visual navigation, image processing algorithms for detection and tracking, and visual SLAM. Finally, the results of applying these techniques in several applications are presented and discussed: this study will encompass power line inspection, mobile target tracking, stereo distance estimation, mapping and positioning.

关键词： UAV Visual servoing Image processing Feature detection tracking SLAM

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Heterogeneous fusion of omnidirectional and PTZ cameras for multiple object tracking

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ieeE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2008年第8期18卷 1052-1063页

作者： Chen, Chung-Hao Yao, Yi Page, David Abidi, Bcsma Koschan, Andreas Abidi, Mongi Univ Tennessee Imaging Robot & Intelligent Syst Lab Knoxville TN 37996 USA

Dual-camera systems have been widely used in surveillance because of the ability to explore the wide field of view (FOV) of the onmidirectional camera and the wide zoom range of the PTZ camera. Most existing algorithms require a priori knowledge of the onmidirectional camera's projection model to solve the nonlinear spatial correspondences between the two cameras. To overcome this limitation, two methods are proposed: 1) geometry and 2) homography calibration, where polynomials with automated model selection are used to approximate the camera's projection model and spatial mapping, respectively. The proposed methods not only improve the mapping accuracy by reducing its dependence on the knowledge of the projection model but also feature reduced computations and improved flexibility in adjusting to varying system configurations. Although the fusion of multiple cameras has. attracted increasing attention, most existing algorithms assume comparable FOV and resolution levels among multiple cameras. Different FOV and resolution levels of the onmidirectional and PTZ cameras result in another critical issue in practical tracking applications. The onmidirectional camera is capable of multiple object tracking while the PTZ camera is able to track one individual target at one time to maintain the required resolution. It becomes necessary for the PTZ camera to distribute its observation time among multiple objects and visit them in sequence. Therefore, this paper addresses a novel scheme where an optimal visiting sequence of the PTZ camera is obtained so that in a given period of time the PTZ camera automatically visits multiple detected motions in a target-hopping manner. The effectiveness of the proposed algorithms is illustrated via extensive experiments using both synthetic and real tracking data and comparisons with two reference systems.

关键词： dual-camera system heterogeneous camera fusion multiple object tracking surveillance system

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iee target tracking 2004: algorithms and applications

IEE Target Tracking 2004: Algorithms and Applications

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iee target tracking 2004: Algoritms and applications

The proceedings contain 19 papers form the conference on iee target tracking 2004: algorithms and applications. The topics discussed include: tracking with persistent, target-related, spurious measurements;practical fusion of quantized measurements via particle filtering;an efficiency particle filter for jump markov nonlinear systems;tracking the manoeuvering targets using multiple scan joint probabilistic data association algorithm;an automatic method for eliminating spurious information from sensor networks;data fusion for several kalman filters tracking a single target;tracking seismic wave;and development of a generic multi-sensor tracking system for agile radars.

关键词： Electric filters

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The iee target tracking 2004: algorithms and applications

The IEE Target Tracking 2004: Algorithms and Applications

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iee target tracking 2004: algorithms and applications

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iee Seminar on target tracking: algorithms and applications

IEE Seminar on Target Tracking: Algorithms and Applications

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iee Seminar on target tracking: algorithms and applications

ISBN: (纸本)086341608X

The proceedings contain 16 papers. The topics discussed include: maintaining track identity for a fragmenting target;performance comparison of tracking algorithms for a ground based radar;exploiting real-time FPGA based adaptive systems technology for real-time sensor fusion in next generation automotive safety systems;a comprehensive study on flight path selection algorithms;evolutionary optimization approach for missile guidance algorithms to achieve accurate interception of maneuvering targets in 3D;incorporating Doppler velocity measurement for track initiation and maintenance;robust background tracking for infrared image sequences;practical use of SIAP metrics in the analysis of air picture quality;keynote address the Viterbi algorithm;and improved target detection and tracking in littoral environments using a selforganizing spatio-temporal CFAR.

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Analysis of selection methods for cost-reference particle filtering with applications to maneuvering target tracking and dynamic optimization

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DIGITAL SIGNAL PROCESSING 2007年第4期17卷 787-807页

作者： Miguez, Joaquin Univ Carlos III Madrid Dept Teoria Senal & Commun Madrid 28911 Spain

Cost-reference particle filtering (CRPF) is a recently proposed sequential Monte Carlo (SMC) methodology aimed at estimating the state of a discrete-time dynamic random system. The estimation task is carried out through the dynamic optimization of a user-defined cost function which is not necessarily tied to the statistics of the signals in the system. In this paper, we first revisit the basics of the CRPF methodology, introducing a generalization of the original algorithm that enables the derivation of some common particle filters within the novel framework, as well as a new and simple convergence analysis. Then, we propose and analyze a particle selection algorithm for CRPF that is suitable for implementation with parallel computing devices and, therefore, circumvents the main drawback of the conventional resampling techniques for particle filters. We illustrate the application of the methodology with two examples. The first one is an instance of one class of problems typically addressed using SMC algorithms, namely the tracking of a maneuvering target using a sensor network. The second example is the application of CRPF to solve a dynamic optimization problem. (c) 2006 Elsevier Inc. All rights reserved.

关键词： sequential Monte Carlo particle filtering resampling stochastic optimization target tracking

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Feature-aided tracking with GMT1 and HRR measurements via mixture density estimation

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iee PROCEEDINGS-CONTROL THEORY AND applications 2006年第3期153卷 342-356页

作者： Ruan, Y. Hong, L. Wright State Univ Dept Elect Engn Dayton OH 45435 USA

tracking ground moving targets with ground moving target indicator and high resolution range (HRR) measurements is becoming increasingly important for many military and civilian applications. We first propose a new HRR information exploitation method using the technique of mixture density estimation. With this technique, features extracted from HRR profiles include not only peak locations and magnitudes, but also the information regarding how energy spreads around peaks. Therefore it is expected to increase significantly the feature discrimination power. We then develop a feature-aided tracking (FAT) algorithm that combines HRR features with traditional kinematic measurements in a probabilistic way. The algorithm does not require any a priori knowledge of target identifications. Simulation results are presented for both the HRR feature extraction method and the FAT algorithm.

关键词： SIMULATION methods & models algorithms MATHEMATICAL programming HIGH resolution electron microscopy SPECTRUM analysis FORCE & energy THEORY of knowledge

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