Robust human detection within a highly dynamic aquatic environment in real time.

This paper presents a real-time foreground detection method for monitoring swimming activities at an outdoor swimming pool. Robust performance and high accuracy of detecting objects-of-interest are two central issues of concern. Therefore, in this paper, a considerable amount of attention has been placed on the following aspects: 1) to establish a better method of modeling aquatic background, which exhibitis dynamic characteristics with random spatial movements, and 2) to establish a method of enhancing the visibility of the foreground by removing specular reflection at nighttime. First, the development of a new background modeling method is reported. In the proposed approach, the background is modeled as a composition of homogeneous blob movements. With an implementation of a spatial searching process, the proposed method shows capability in associating and distinguishing movements caused by the background. Hence, this contributes to better performance in foreground detection. On the issue of enhancing the visibility of the foreground, a decision-based filtering scheme is proposed as a preprocessing step. A defined concept term, fluctuation measure, is defined for classifying each pixel to be one of the predefined types. This has allowed suitable spatial or spatiotemporal filters to be applied accordingly for color the compensation step. All of these developments are evaluated by testing live on a busy Olympic-size outdoor public swimming pool. Both qualitative and quantitative evaluations are reported. This provides a comprehensive study of the system.     

Calibration of tracking systems in a surgical environment

The purpose of this paper was to assess to what extent an optical tracking system (OTS) used for position determination in computer-aided surgery (CAS) can be enhanced by combining it with a direct current (DC) driven electromagnetic tracking system (EMTS). The main advantage of the EMTS is the fact that it is not dependent on a free line-of-sight. Unfortunately, the accuracy of the EMTS is highly affected by nearby ferromagnetic materials. The authors have explored to what extent the influence of the metallic equipment in the operating room (OR) can be compensated by collecting precise information on the nonlinear local error in the EMTS by using the OTS for setting up a calibration look-up table. After calibration of the EMTS and registration of the sensor systems in the OR the authors have found the average euclidean deviation in position readings between the DC tracker and the OTS reduced from 2.9±1.0 mm to 2.1±0.8 mm within a half-sphere of 530-mm radius around the magnetic field emitter. Furthermore the authors have found the calibration to be stable after re-registration of the sensors under varying conditions such as different heights of the OR table and varying positions of the OR equipment over a longer time interval. These results encourage the further development of a hybrid magnetooptical tracker for computer-aided surgery where the electromagnetic tracker acts as an auxiliary source of position information for the optical system. Strategies for enhancing the reliability of the proposed hybrid magnetooptic tracker by detecting artifacts induced by mobile ferromagnetic objects such as surgical tools are discussed     

高动态微弱信标信号载波捕获跟踪的设计与实现

为了研究高动态低信噪比下信标信号的载波捕获跟踪问题,采用两级二阶锁频环(FLL)和一级三阶锁相环(PLL)技术方案。FLL采用基于周期图的鉴频方法,实现极低信噪比下信号载波的捕获。PLL中的三阶锁相环通过对环路压控振荡器(VCO)的输入控制信号进行监测,自适应地调整环路滤波器的带宽,以满足高动态下信号载波频率动态范围大的要求。在基于FPGA构建的信号处理板上验证了上述实现方案,并给出实验结果。     

Joint audio-video object localization and tracking

There has been a tremendous amount of research on object localization either involving microphone arrays or video cameras. Considerable less attention has been paid, however, to object localization and tracking based on joint audio-video processing thus far. This may be related to the lack of suitable algorithms for object localization simultaneously using multimicrophone outputs and color image sequences. In this article, we propose a solution to this problem. Before elaborating on joint audio-video processing, we review some previous work the areas of audio and video object localization. Then a recursive sensor fusion method based on decentralized Kalman filtering is introduced. Unfortunately, the decentralized Kalman filter cannot be directly used for joint audio-video object localization due to specific properties of the audio sensor. By properly adjusting the local audio position estimator, however, we manage to keep the overall architecture. We stress the general methodology     

Robust player detection and tracking in broadcast soccer video based on enhanced particle filter

It is significant to detect and track soccer players in broadcast sports video, which is helpful to analysis player activity and team tactics. However, it is challenging to efficiently detect and track soccer players with shots switched and noise caused by auditorium and billboards. And for multi-player tracking how to treat the increase or decrease of player are also difficult. In this paper, a robust player detection algorithm based on salient region detection and tracking based on enhanced particle filtering are proposed. Salient region detection is used to segment sports fields, and then soccer players are detected by edge detection combined with Otsu algorithm. For soccer players tracking, we use an enhanced particle filter which we improve the algorithm in sample and the likelihood function combing the color feature and edge feature. Experimental results show the proposed algorithm can quickly and accurately detect and track soccer players in broadcast video.     

Source localization using vector sensor array in a multipath environment

Coherent signals from distinct directions is a natural characterization of the multipath propagation effect. This paper addresses the problem of coherent/fully correlated source localization using vector sensor arrays. The maximum likelihood (ML) and minimum-variance distortionless response (MVDR) estimators for source direction-of-arrival (DOA) and signal polarization parameters are derived. These estimators require no search over the polarization parameters. In addition, a novel method for "decorrelating" the incident signals is presented. This method is based on the polarization smoothing algorithm (PSA) and enables the use of eigenstructure-based techniques, which assume uncorrelated or partially correlated signals. The method is implemented as a preprocessing stage before applying eigenstructure-based techniques, such as MUSIC. Unlike other existing preprocessing techniques, such as spatial smoothing and forward-backward (FB) averaging, this method is not limited to any specific array geometry. The performance of the proposed PSA preprocessing combined with MUSIC is evaluated and compared to the Crame/spl acute/r-Rao Bound (CRB) and the ML and MVDR estimators. Simulation results show that the MVDR and PSA-MUSIC asymptotically achieve the CRB for a scenario with two coherent sources with and without an uncorrelated interference source. A sensitivity study of PSA-MUSIC to source polarization was also conducted via simulations.     

Text detection,localization, and tracking in compressed video

Video text information plays an important role in semantic-based video analysis, indexing and retrieval. Video texts are closely related to the content of a video. Usually, the fundamental steps of text-based video analysis, browsing and retrieval consist of video text detection, localization, tracking, segmentation and recognition. Video sequences are commonly stored in compressed formats where MPEG coding techniques are often adopted. In this paper, a unified framework for text detection, localization, and tracking in compressed videos using the discrete cosines transform (DCT) coefficients is proposed. A coarse to fine text detection method is used to find text blocks in terms of the block DCT texture intensity information. The DCT texture intensity of an 8×8 block of an intra-frame is approximately represented by seven AC coefficients. The candidate text block regions are further verified and refined. The text block region localization and tracking are carried out by virtue of the horizontal and vertical block texture intensity projection profiles. The appearing and disappearing frames of each text line are determined by the text tracking. The final experimental results show the effectiveness of the proposed methods.     

Improving the security of a dynamic look-up table based chaotic cryptosystem

In this brief, we analyze the cause of vulnerability of the original dynamic look-up table based chaotic encryption scheme in detail, and then propose the corresponding enhancement measures. Theoretical analysis and computer simulation indicate that the modified scheme is more secure than the original one.     

Blind high-resolution localization and tracking of multiplefrequency hopped signals

This paper considers the problem of blind localization and tracking of multiple frequency-hopped spread-spectrum signals using a uniform linear antenna array without knowledge of hopping patterns or directions of arrival. As a preprocessing step, we propose to identify a hop-free subset of data by discarding high-entropy spectral slices from the spectrogram. High-resolution localization is then achieved via either quadrilinear regression of four-way data generated by capitalizing on both spatial and temporal shift invariance or a new maximum likelihood (ML)-based two-dimensional (2-D) harmonic retrieval algorithm. The latter option achieves the best-known model identifiability bound while remaining close to the Cramer-Rao bound even at low signal-to-noise ratios (SNRs). Following beamforming using the recovered directions, a dynamic programming approach is developed for joint ML estimation of signal frequencies and hop instants in single-user tracking. The efficacy of the proposed algorithms is illustrated in pertinent simulations     

An adaptive tracking algorithm for direction finding and array shape estimation in a nonstationary environment

This paper presents an adaptive tracking algorithm for the subspace-based direction of arrival estimation of multiple sources in a nonstationary, environment. The nonstationarities are due to moving sources or to timevarying distortions of the sensor array shape. The proposed algorithm relies on the properties of a linear operator, referred to as the Propagator, which only exploits the linear independency of the source steering vectors. The Propagator allows not only the calibration of the array shape, but also the determination of the source and the noise subspaces without any eigendecomposition of the cross-spectral matrix of the received signals. A gradientbased adaptive algorithm is here proposed for the on-line estimation of the Propagator. A theoretical analysis of the behaviour of this algorithm in a nonstationary environment is given. Simulations are carried out in the case of moving sources and in the case of a time-varying array shape. They exhibit the good performances of the proposed algorithm.     

Design and analysis of dynamic mobility tracking in wirelesspersonal communication networks

In personal communication networks (PCN), mobility tracking provides the function of locating a mobile user within the service area. We propose an intersection-oriented dynamic location update (LU) strategy, which is designed to minimize the cost of mobility tracking by optimally partitioning the service area into location registration (LR) areas on a per-user basis. A realistic mobility model is proposed in which we consider not only the mobility behavior of the individual subscriber, but also the street layout. Based on this model, a transient Markov process is developed to analyze the tracking cost. To reduce the implementation cost and to make the proposed strategy feasible, the concept of mobile subscriber (MS) grouping, and equivalent quadrangular LR area are also adopted     

线性调频信号辐射源的唯方位跟踪

对宽带线性调频信号的方位角估计以及对这类信号辐射源单站跟踪问题进行了研究.算法通过对称阵元Wigner-Ville分布(WVD),建立了一种新颖的具有分维结构的空间时频分布数据模型,并利用Beamspace-ESPRIT和子空间投影法估计信号瞬时频率向量和到达角.在到达角估计的基础上,针对在直角坐标系中利用扩展的卡尔曼滤波器的唯方位跟踪法不稳定问题,采用了修改的极坐标,实现了运动单站对运动辐射源的稳定和渐进无偏的定位与跟踪.     

动态图像跟踪优化算法的研究与仿真

传统图像跟踪算法中,跟踪的图像搜索过程需要历遍所有特质,在图像场景较为复杂的情况下,在"无用"匹配点上耗费大量计算时间,跟踪过程误差较大。提出一种适用于复杂场景下动态图像跟踪优化算法,选择在复杂场景下鲁棒性较强的参数,以增加复杂场景下目标描述的信息量和稳定性;引入一种MAD(平均绝对差)匹配算法:在进行动态图像跟踪过程中采用MAD算法和鲁棒性参数相结合,通过动态帧图像和静态帧对比量即MCD(最多临近点),设定跟踪阈值,通过选取后的图像实现动态图像的运动目标跟踪。仿真实验结果表明,提出方法的跟踪精度对比传统方法有明显提高。     

The living virtual Kinka Kuji temple: a dynamic environment

Our research uses the natural computing power of emergent properties and chaos found in live data streams to bring a “living” element into a heritage environment. Using a live chaotic data stream found on the Internet, we can produce emergent rhythmic behaviors to control artificial life and climatic environmental changes. We constructed a model of the Golden Temple (Kinka Kuji) in Kyoto, Japan for our experiment. We decided to design artificial life “fireflies” to inhabit the neighboring temple gardens. These fireflies are connected in real time to the actions of stocks environments in the Nasdaq stock market. Using live chaotic data streams holds a lot of promise in providing a more natural process for both virtual environments and artificial life. In this research, we've only explored its beginning potentials, searching for the types of data available from users and the Internet. The data can provide new perspectives in making our virtual heritage alive and be closer to interpreting the “magical nature” of the heritage site     

A novel dynamic localisation system for indoor and outdoor tracking

ABSTRACT

Hybrid indoor and outdoor localisation system has generated substantial interest from researchers due to its potential use in many fields. Many research papers on hybrid localisation were focused on localisation accuracy and computation time instead of the process for seamless switching during the subject’s indoor-outdoor movement. Seamless and stable switching can improve the usability and practicality of a system in an ever-changing scenario. This paper presents a hybrid localisation system that can adapt to the changes in the environment. Two hybrid systems – Enhanced Fusion Hybrid (EFH) and Enhanced Unified Hybrid (EUH), and novel signal selection and switching algorithms suitable for dynamic environment where signal fluctuations and transient condition frequently occur, i.e. the Clean Radio Signal Label (CRSL) classification algorithm and the Adaptive Signal Thresholding (AST) algorithm are proposed. From the experimental results of EFH and EUH methods, the GPS and Wi-Fi combination produces the best results as compared to the combinations of GPS and GSM or GSM and Wi-Fi. The accuracy of estimates was improved to 2.0 m to 5.0 m. Further work shown that an accuracy of 2.0 m or less could be achieved with an increase in Wi-Fi nodes from three to four for the system.     

学习质量动态跟踪系统的设计与实现

随着反转课堂这种新的教学模式在大学的推广,对学生的考核方式也由传统的学习结果考核转向对学习过程的考核,针对以过程性考核为主的考核制,深入研究教学过程的需求,充分利用智能手机等移动终端,提出了一种适应网络化教学的可定制评分项目的实时评分系统模型,设计并实现了基于Android的学习质量动态跟踪系统。该系统完成了教师自行定制评分模块并进行打分,学生实时查看成绩等主要功能,系统可以对教学过程中的成绩进行分析,对学生提出学业预警。经过一学期的测试,验证了该系统的可靠性和安全性。     

EEG-based assessment of driver cognitive responses in a dynamic virtual-reality driving environment

Accidents caused by errors and failures in human performance among traffic fatalities have a high death rate and become an important issue in public security. They are mainly caused by the failures of the drivers to perceive the changes of the traffic lights or the unexpected conditions happening accidentally on the roads. In this paper, we devised a quantitative analysis for assessing driver's cognitive responses by investigating the neurobiological information underlying electroencephalographic (EEG) brain dynamics in traffic-light experiments in a virtual-reality (VR) dynamic driving environment. The VR technique allows subjects to interact directly with the moving virtual environment instead of monotonic auditory and visual stimuli, thereby provides interactive and realistic tasks without the risk of operating on an actual machine. Independent component analysis (ICA) is used to separate and extract noise-free ERP signals from the multi-channel EEG signals. A temporal filter is used to solve the time-alignment problem of ERP features and principle component analysis (PCA) is used to reduce feature dimensions. The dimension-reduced features are then input to a self-constructing neural fuzzy inference network (SONFIN) to recognize different brain potentials stimulated by red/green/yellow traffic events, the accuracy can be reached 87% in average eight subjects in this visual-stimuli ERP experiment. It demonstrates the feasibility of detecting and analyzing multiple streams of ERP signals that represent operators' cognitive states and responses to task events.     

On the tracking of rapid dynamic changes in seizure EEG

Estimation of autospectra and coherence and phase spectra of the seizure electroencephalograph (EEG), using the fast Fourier transform (FFT) technique, will cause smearing of the rapid dynamic changes which occur during the seizure. This is inherent to FFT spectral estimation, due to the averaging process which is necessary in order to get consistent spectral estimates. A different approach suggested in the present study is to carry out multivariate autoregressive modeling of the multichannel seizure EEG, combined with adaptive segmentation. In order to obtain good estimates in cases of short record length, the vectorial autoregressive (AR) modeling was based on residual energy ratios. The method has been tested on multichannel seizure EEG recordings from rats with focal epilepsy, caused by intracerebral administration of Kainic acid, and in-depth EEG recordings in patients with temporal lobe epilepsy     

地面动态跟踪精度试验中的坐标系变换研究

为了简便有效地对光电产品进行检测,在地面模拟飞机实际飞行环境下进行地面运动目标跟踪精度试验,提出了将目标的GPS位置信息转换为产品坐标系下的坐标的变换方法,经过Matlab仿真计算,这种变换方法得到的目标基于产品的方位、俯仰角符合实际情况,适合计算跟踪精度。