Description: the attached file consists of matlab code for implementation of sequential importance sampling particle filter given in IEEE paper entitled as "A TUTORIAL ON PARTICLE FILTERS FOR ONLINE NONLINEAR NON GUASSIAN BAYESIAN TRACKING" Platform: |
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Author:babi |
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Description: 通过单一的Wi-Fi接入点的信号强度来判断移动物体的位置。比较新的一篇文章。用了蒙特卡罗抽样的办法－Monte Carlo Sampling Method－来估计位置。-This paper describes research towards a system
for locating wireless nodes in a home environment requiring
merely a single access point. The only sensor reading used for
the location estimation is the received signal strength indication
(RSSI) as given by an RF interface, e.g.,Wi-Fi.Wireless
signal strengthmaps for the positioning filter are obtained by
a two-step parametric and measurement driven ray-tracing
approach to account for absorption and reflection characteristics
of various obstacles. Location estimates are then
computed using Bayesian filtering on sample sets derived
by Monte Carlo sampling. We outline the research leading
to the system and provide location performance metrics using
trace-driven simulations and real-life experiments. Our
results and real-life walk-troughs indicate that RSSI readings
from a single access point in an indoor environment
are sufficient to derive good location estimates of users with
sub-room precision. Platform: |
Size: 452608 |
Author:weihuagao |
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Description: CVPR2012_oral
Weakly Supervised Structured Output Learning for Semantic Segmentation-We address the problem of weakly supervised semantic
segmentation. The training images are labeled only by the
classes they contain, not by their location in the image. On
test images instead, the method must predict a class label
for every pixel. Our goal is to enable segmentation algorithms
to use multiple visual cues in this weakly supervised
setting, analogous to what is achieved by fully supervised
methods. However, it is difficult to assess the relative usefulness
of different visual cues from weakly supervised training
data. We define a parametric family of structured models,
where each model weighs visual cues in a different way. We
propose a Maximum Expected Agreement model selection
principle that evaluates the quality of a model from the family
without looking at superpixel labels. Searching for the
best model is a hard optimization problem, which has no
analytic gradient and multiple local optima. We cast it as
a Bayesian optimization problem and propose an Platform: |
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Author:费炳超 |
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Description: “Fast Tracking via Dense Spatio-Temporal Context Learning,” In ECCV 2014的源代码，效果非常好。-In this paper, we present a simple yet fast and robust algorithm which exploits the spatio-temporal context for visual tracking. Our approach formulates the spatio-temporal relationships between the object of interest and its local context based on a Bayesian framework, which models the statistical correlation between the low-level features (i.e., image intensity and position) from the target and its surrounding regions. The tracking problem is posed by computing a confidence map, and obtaining the best target location by maximizing an object location likelihood function. The Fast Fourier Transform is adopted for fast learning and detection in this work. Implemented in MATLAB without code optimization, the proposed tracker runs at 350 frames per second on an i7 machine. Extensive experimental results show that the proposed algorithm performs favorably against state-of-the-art methods in terms of efficiency, accuracy and robustness. Platform: |
Size: 8955904 |
Author:happy |
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Description: 关于无人机控制的论文，比较有价值，推荐给大家-Abstract—We introduce an intelligent cooperative control
system for ground target tracking in a cluttered urban environment
with a team of Unmanned Air Vehicles (UAVs). We
extend the work of Yu et. al. [1] to add a machine learning
component that uses observations of target position to learn a
model of target motion. Our learner is the Sequence Memoizer
[2], a Bayesian model for discrete sequence data, which we use
to predict future target location identifiers, given a context of
previous location identifiers. Simulated cooperative control of
a team of 3 UAVs in a 100-block city filled with various sizes
of buildings verifies that learning a model of target motion can
improve target tracking performance. Platform: |
Size: 1165312 |
Author:王日俊 |
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Description: 感知行为的影响因素包括单个关节的动作和不同关节的组态。因此提出一种新的基于关节的位置差异的特征类型，联合包括静态姿势、动作、位移在内的行为信息进行识别。采用关节在两个时间和空间区域的差异来明确地模拟个别关节动力学和不同关节的组态。然后应用主成分分析（PCA）来获得所需的特征。同时应用非参数的简捷的贝叶斯最近邻（NBNN）分类器进行多类行为的分类。这个NBNN分类器避免了帧描述符的量化，计算“图像到类别”的距离而不是“图像到图像”的距离。15到20帧的数据就足以实现手势以及动作的识别，无需应用整个视频序列，极大地缩短了延时，提高了识别效率。-Perceived behavioral factors, including single-joint movements and configure different joints. Therefore suggests a new location-based differences in the characteristics of the type of joint, the joint including a static posture, movement, displacement including behavioral information for identification. Differences in the use of the joint region both time and space explicitly configured to simulate individual joints kinetics and different joints. Then principal component analysis (PCA) to obtain the desired characteristics. At the same time a simple nonparametric Bayesian nearest neighbor (NBNN) classifier to classify many types of behavior. This avoids the frame NBNN classification descriptors to quantify computing " image to the category" distance rather than distance " image to the image" of. Data 15-20 is sufficient to achieve the gesture and action recognition, without application of the entire video sequence, which greatly reduces the latency and improve the ef Platform: |
Size: 38912 |
Author:杨杨 |
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Description: 输入文字或网址，即可翻译
基于贝叶斯网的IP网络故障定位算法的研究和实现_董海疆
Study on fault location algorithm of Bayesian network IP network and realize the _ based on Dong Haijiang
-输入文字或网址，即可翻译
基于贝叶斯网的IP网络故障定位算法的研究和实现_董海疆
基于贝叶斯网的IP网络故障定位算法的研究和实现_董海疆
Study on fault location algorithm of Bayesian network IP network and realize the _ based on Dong Haijiang
Platform: |
Size: 3475456 |
Author:fdsfdsf |
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Description: In this paper, we present a simple yet fast and robust algorithm which exploits the spatio-temporal context for visual tracking. Our approach formulates the spatio-temporal relationships between the object of interest and its local context based on a Bayesian framework, which models the statistical correlation between the low-level features (i.e., image intensity and position) from the target and its surrounding regions. The tracking problem is posed by computing a confidence map, and obtaining the best target location by maximizing an object location likelihood function. The Fast Fourier Transform is adopted for fast learning and detection in this work. Implemented in MATLAB without code optimization, the proposed tracker runs at 350 frames per second on an i7 machine. Extensive experimental results show that the proposed algorithm performs favorably against state-of-the-art methods in terms of efficiency, accuracy and robustness. Platform: |
Size: 7226368 |
Author:大大大大大澈儿 |
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Description: 室内定位，贝叶斯定位算法，使用matlab编写(Indoor location
Bayesian location algorithm
The program is written in MATLAB.) Platform: |
Size: 3072 |
Author:你管我叫啥 |
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Search - Bayesian Location