Description: A new approach toward target representation and localization, the central component in visual tracking
of non-rigid objects, is proposed. The feature histogram based target representations are regularized
by spatial masking with an isotropic kernel. The masking induces spatially-smooth similarity functions
suitable for gradient-based optimization, hence, the target localization problem can be formulated using
the basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyya
coefficient as similarity measure, and use the mean shift procedure to perform the optimization. In the
presented tracking examples the new method successfully coped with camera motion, partial occlusions,
clutter, and target scale variations. Integration with motion filters and data association techniques is also
discussed. We describe only few of the potential applications: exploitation of background information,
Kalman tracking using motion models, and face tracking.-A new approach toward target representation and localization, the central component in visual trackingof non-rigid objects, is proposed. The feature histogram based target representations are regularizedby spatial masking with an isotropic kernel. The masking induces spatially-smooth similarity functionssuitable for gradient-based optimization, hence, the target localization problem can be formulated usingthe basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyyacoefficient as similarity measure, and use the mean shift procedure to perform the optimization. In thepresented tracking examples the new method successfully coped with camera motion, partial occlusions, clutter, and target scale variations. Integration with motion filters and data association techniques is alsodiscussed. We describe only few of the potential applications: exploitation of background information, Kalman tracking using motion models, and face tracking . Platform: |
Size: 2779136 |
Author: |
Hits:
Description: A new approach toward target representation and localization, the central component in visual tracking of nonrigid objects,
is proposed. The feature histogram-based target representations are regularized by spatial masking with an isotropic kernel. The
masking induces spatially-smooth similarity functions suitable for gradient-based optimization, hence, the target localization problem
can be formulated using the basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyya coefficient as
similarity measure, and use the mean shift procedure to perform the optimization. In the presented tracking examples, the new method
successfully coped with camera motion, partial occlusions, clutter, and target scale variations. Integration with motion filters and data
association techniques is also discussed. We describe only a few of the potential applications: exploitation of background information,
Kalman tracking using motion models, and face tracking. Platform: |
Size: 2459648 |
Author:Ali |
Hits:
Description: 正交相关目标检测，采用正交相关方法检测指定目标在图像中的位置。适用于计算机视觉中的视频目标检测、视觉目标检测、目标定位、视觉目标跟踪、视频目标跟踪、图像匹配、图像配准等工作。-cross relation detection is used to detect object in image for in the field computer vision such as visual object detection, motion detection, object localization, visual object tracking, video object tracking, image registration, image matching, etc. Platform: |
Size: 109568 |
Author:朱亮亮 |
Hits:
Description: 人脸视频图像编码是近年来图像编码领域里的一个研究热点问题,它在通信、互联网等方面有着广泛的应用前景。人脸图像编码的研究包含很多子问题,主要的三个方面为:精确的人脸目标定位算法,实时的人脸目标跟踪算法和高效的人脸图像编码方法。本文的主要研究工作在于: 1) 提出帧间差分和背景差分相结合的人脸目标定位算法,在人脸特征选择上,主要使用形状特征,通过椭圆拟合来得到人脸的位置。 2) 采用基于Kalman滤波的运动预测方法对人脸的区域变化进行预测,以此缩小目标可能存在的区域(ROI).-Facial video image coding image compression in recent years a hot research topic in the field of the problem, it is in the communication, the Internet have a wide range of applications. Coding of face images contain many sub-problems, the main three are: accurate target location algorithm for face, real-time face tracking algorithm and efficient facial image coding method. This major research work are: 1) of the frame difference and background difference goal of combining face localization algorithm, feature selection in the human face, the main use of shape features, by ellipse fitting to get the location of the face. 2) Kalman filtering based motion prediction method changes the face of the region predicted to narrow the possible target area (ROI). Platform: |
Size: 1024 |
Author:周参 |
Hits:
Description: 基于拟牛顿法的双基地目标定位算法，定位精度还可以-Newton method based on dual-base target localization algorithm, positioning accuracy can also be Platform: |
Size: 1024 |
Author:宋公 |
Hits:
Description: 研究了存在系统误差时单站纯方位角无源定位系统的定位问题，分别在系统误差为常值和时变值时研
究了TLS-KF算法的性能，并通过估计系统误差是否存在，给出了一种选择最优定位算法的判决方法。该问题的
研究对于单站无源定位系统如何在存在系统误差时提高定位精度和性能，具有一定的理论意义和实际意义。
-The single station bearing-only passive target localization was studied when system bias was
existent.The performance of TLS-KF was studied vias simulations in the cases that the system bias was a
constant and time variant respectively.In order to choose the optimum localization algorithm,a criterion was
given to estimate whether the system bias was existent.The study of this paper is helpful in practical target
localization.
Platform: |
Size: 914432 |
Author:张洋 |
Hits:
Description: 语音分离的研究在语音通信、声学目标检测、声音信号增强等方面有着重要的理论意义和实用价值。而将语音分离技术应用到智能机器人中。让机器人具有智能的听觉，实现声源定位和分离，确定说话人个数，进行人机对话等方面更具有广阔的应用前景。 -Separation of speech in voice communications, acoustic target detection, sound signal enhancement, and so has important theoretical significance and practical value. The voice separation technology applied to intelligent robots. Intelligent robot to the hearing, to achieve sound localization and separation, to determine the number of speaker, the man-machine dialogues in a more broad application prospects. Platform: |
Size: 23552 |
Author:liu |
Hits:
Description: 文章针对低信噪比下的水下目标定位问题，建立了水下无线传感器阵列网络，该结构包括多个分布式声传感器阵列，它适应于多模态信号处理，既可以利用目标的方位信息，又可以用能量信息。文中提出了用每个阵列接收到的信号能量作为参量完成目标定位并推导了基于能量的最大似然比目标定位方法。数值仿真表明：基于该结构的能量似然函数定位方法，可以有效估计目标的位置。并且比单阵元网络的定位性能和信息传输率上有了较大的提高， 尤其是在低信噪比下情况下，可以大大减小估计的方差。-With novel underwater wireless sensor array network (UWSAN) architecture that consists of multiple distributed arrays of acoustic sensors, maximum likelihood localization based on acoustic energy is proposed for solving the source localization in underwater and low signal to noise ratio. The exactly maximum likelihood (ML) target location estimator is derived. Very impressive simulation results demonstrated the feasibility of such a new approach for underwater wireless sensor network (UWSN). And comparison with the single sensor UWSN, the performance of localization is improved, especially in low SNR. Platform: |
Size: 265216 |
Author:于文娟 |
Hits:
Description: Monitoring applications define an important
class of applications used in wireless sensor networks.
In these applications the network perceives
the environment and searches for event
occurrences (phenomena) by sensing different
physical properties, such as temperature, humidity,
pressure, ambient light, movement, and presence
(for target tracking). In such cases the
location information of both phenomena and
nodes is usually required for tracking and correlation
purposes. In this work we summarize most
of the concepts related to localization systems for
WSNs as well as how to localize the nodes in
these networks (which allows the localization of
phenomena). By dividing the localization systems
into three distinct components — distance/angle
estimation, position computation, and localization
algorithm — besides providing a didactic
viewpoint, we show that these components can
be seen as subareas of the localization problem
that need to be analyzed and studied separately. Platform: |
Size: 134144 |
Author:ginanjar |
Hits:
Description: 背景加权直方图算法（BWH）在[2]中提出了尝试
减少干扰的背景均值漂移跟踪的目标定位。然而，
在本文中，我们证明了权重分配给候选目标区域的像素
BWH是那些没有背景资料成正比，即不会引入BWH
任何新的信息，因为均值漂移迭代公式是不变的规模
改造砝码。然后，我们提出了一个校正BWH（CBWH）的公式
只转型的目标模式，但不是目标候选模型。 CBWH计划
可以有效地降低背景的干扰，在目标定位。实验
结果表明，CBWH可能会导致更快的收敛速度和更准确的定位比
通常的目标表示均值漂移跟踪。即使目标没有得到很好的初始化，
该算法仍然强劲跟踪的对象，这是很难实现由
传统的目标表示。-The background-weighted histogram (BWH) algorithm proposed in [2] attempts to
reduce the interference of background in target localization in mean shift tracking. However,
in this paper we prove that the weights assigned to pixels in the target candidate region by
BWH are proportional to those without background information, i.e. BWH does not introduce
any new information because the mean shift iteration formula is invariant to the scale
transformation of weights. We then propose a corrected BWH (CBWH) formula by
transforming only the target model but not the target candidate model. The CBWH scheme
can effectively reduce background’s interference in target localization. The experimental
results show that CBWH can lead to faster convergence and more accurate localization than
the usual target representation in mean shift tracking. Even if the target is not well initialized,
the proposed algorithm can still robustly track the object, which is hard to achieve by the
conventiona Platform: |
Size: 730112 |
Author:吴盈 |
Hits:
Description: A new approach toward target representation and localization, the central component in visual tracking of nonrigid objects,
is proposed. The feature histogram-based target representations are regularized by spatial masking with an isotropic kernel. The
masking induces spatially-smooth similarity functions suitable for gradient-based optimization, hence, the target localization problem
can be formulated using the basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyya coefficient as
similarity measure, and use the mean shift procedure to perform the optimization. In the presented tracking examples, the new method
successfully coped with camera motion, partial occlusions, clutter, and target scale variations. Integration with motion filters and data
association techniques is also discussed. We describe only a few of the potential applications: exploitation of background information,
Kalman tracking using motion models, and face tracking. Platform: |
Size: 2439168 |
Author:Felix |
Hits:
Description: We propose an approximation framework for distributed target
localization in sensor networks. We represent the unknown
target positions on a location grid as a sparse vector,
whose support encodes the multiple target locations.
The location vector is linearly related to multiple sensor
measurements through a sensing matrix, which can be locally
estimated at each sensor. We show that we can successfully
determine multiple target locations by using linear
dimensionality-reducing projections of sensor measurements.
The overall communication bandwidth requirement
per sensor is logarithmic in the number of grid points and
linear in the number of targets, ameliorating the communication
requirements. Simulations results demonstrate the performance
of the proposed framework. Platform: |
Size: 1075200 |
Author:saeede abbasi |
Hits:
Description: 目标定位的matlab程序，用的是经典的质心定位法，参考价值很高，参数已经全部设置好，下载后可以直接顺利运行。-Target localization using matlab program, the centroid localization method is the classic, very high reference value, the parameters have been set up well, can be directly downloaded to run smoothly. Platform: |
Size: 2048 |
Author:czm |
Hits:
Description: [Matlab] 模拟无人机定位目标。这里无人机按sin曲线运行，运用EKF,UKF,PF方法进行滤波，对随机目标进行定位并展示定位过程。-[MATLAB] Simulation of Localization by UAV. It uses Extended Kalman Filter, Unscented Kalman Filter and Particle Filter to find the localization of target. Platform: |
Size: 87040 |
Author:Siqi |
Hits:
Description: Localization is one of the key techniques in wireless sensor network. The location estimation methods can be classified into target/source localization and node self-localization. In target localization, we mainly introduce the energy-based method. Then we investigate the node self-localization methods. Since the widespread adoption of the wireless sensor network, the localization methods are different in various applications. And there are several challenges in some special scenarios. In this paper, we present a comprehensive survey of these challenges: localization in non-line-of-sight, node selection criteria for localization in energy-constrained network, scheduling the sensor node to optimize the tradeoff between localization performance and energy consumption, cooperative node localization, and localization algorithm in heterogeneous network. Finally, we introduce the evaluation criteria for localization in wireless sensor network. Platform: |
Size: 1106548 |
Author:atrakpc@yahoo.com |
Hits:
Description: The function to view the multiple radars layout
Inputs:
% ang: angular distribution type
% rng: geographical distribution type
% trg: 2x1 vector for the position of target in meters
Outputs:
% M: the number of transmit radars
% N: the number of receive radars
% tx and ty: position of transmit radars in meters for x-y plane
% rx and ry: position of receive radars in meters for x-y plane Platform: |
Size: 30847 |
Author:Navya_Ravikumar |
Hits:
Search - target localization