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Description: 鲁棒人脸跟踪系统学位论文；提出了一种两阶段的光照均衡的方法来消除单幅图像中的各种阴影包括模糊阴影，投射阴影等 根据快速的Adaboost 训练框架，本文提出了一个实时的鲁棒人脸检测算法 提出了一种基于在线实值boosting 的方法来处理人脸在跟踪过程中发生的外貌变化；实现了一个基于以上模块的完全自动化的人脸跟踪器-Robust Face Tracking System dissertation proposes a two-stage light-balanced approach to the elimination of single image in a variety of shadow including fuzzy shadow, projected shadow, etc. Under the fast-track the Adaboost training framework, this paper presents a real-time Robust Face Detection Algorithm Based on boosting online real ways to deal with people face in tracking occurred during the appearance of change the realization of a module based on the above fully automated face tracker
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Author: 田卉 |
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Description: STEREO-BASED ELLIPTICAL HEAD TRACKING-A novel algorithm for head-tracking based on stereo is
presented in this paper. Employing stereo vision makes the
tracker robust to many factors such as clutter, color and lighting
variations, that affect contemporary head trackers
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Author: x |
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Description: 本文研究的是设计一个控制系统，用固定在云台上的摄像头来跟踪运动目标。具体包括硬件设计和软件编程。利用计算机、摄像机、图像采集卡、步进电机驱动器和步进电机等硬件设备设计一套视觉自动跟踪系统，利用VC++实现上位机串口通信程序；利用KeilC51实现下位机的控制功能-This paper is to design a control system, using a fixed camera in the head Yuntai tracking moving targets. Including hardware design and software programming. The use of computers, cameras, frame grabbers, stepper motor and stepper motor drives and other hardware to design a visual tracking system, using VC++ realization of PC serial communication procedures use KeilC51 lower the achievement of the control machine
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Size: 419840 |
Author: 杨俊 |
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Description: 关于运动物体跟踪的论文，ameanshifttrackerimplementations-Moving object tracking on the paper, a-mean-shift-tracker-implementations
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Author: shujuan |
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Description: "Bayesian Mean Shift Face Tracker"
This is a paper about tracking by particle filter.
This is very useful.
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Size: 499712 |
Author: Wolf |
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Description: 块相位估计－Block Phase Estimation(BPE)取代了传统的锁相环(PLL)结构，以取模 (Modulo)运算为基础，而且使用创新的平均器，既简化了算法结构，又大大方便于硬件实现，同时为芯片设计提出了新的思路；但国内研究较少，资料极其匮乏；文章在对BPE的原理与结构进行较为深入的介绍与分析的基础上，对BPE进行了由浅入深的仿真，解决了部分跟踪器－SECTOR TRACKER(ST)的问题，给出了部分仿真结果；最后，分析了BPE的优缺点，给出了改进的方法。-Block Phase Estimation(BPE) replaces the traditional phase locked loop(PLL), is based on Modulo operation and makes use of the innovative averager ,which simplifies the construction of the arithmetic and facilitates the hardware implementation a lot and presents a new thinking for chip design But interiorly there are few researthes and the data are extremely scarce Based on an in-depth introduction and analysis of the principle and construction of BPE,the paper emulates BPE from simple to deep,solves the problem of SECTOR TRACKER(ST),and provides emulation results At last,the paper specifies the advantage and disadvantage of BPE,and introduces the improving methods.
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Author: 郭飞 |
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Description: Tracking people in a dense crowd is a challenging problem
for a single camera tracker due to occlusions and extensive
motion that make human segmentation difficult. In this
paper we suggest a method for simultaneously tracking all
people in a dense crowd using a set of cameras with overlapping
fields of view. To overcome occlusions, the cameras
are placed at a high elevation and only people’s heads
are tracked. Head detection is still difficult since each foreground
region may consist of multiple subjects. By combining
data from several views, height information is extracted
and used for head segmentation.
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Author: kaw |
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Description: 2011 paper in IEEE in maximum power point tracker
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Size: 102400 |
Author: Roshan |
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Description: 在线boost算法，来自文章On-line Boosting and Vision-boost tracker
from paper::
On-line Boosting and Vision
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Size: 73728 |
Author: zhang |
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Description: Maximum power point tracking (MPPT) must usually be integrated with photovoltaic (PV) power
systems so that the photovoltaic arrays are able to deliver the maximum power available.
The present paper proposes a maximum power point tracker (MPPT) method, based on fuzzy logic
controller (FLC), applied to a stand-alone photovoltaic system under variable temperature and irradiance conditions. The objective of this controller is to extract the maximum power of photovoltaic modules.
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Author: Ninouh |
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Description: This paper considers the oretical and practical
implementation of the a-p tracking process under the
influence of track rate variations and/or track period
perturbations. These considerations are necessary
when the a-p tracker is used in The Benchmark 96
Target Tracking Problem [l-21, a challenge to the
tracking community to generate tracking solutions to
for a set of maneuvering targets under the influence of
False Alarms (FA), Mis-Detections (MD) and Electronic
Counter-Measures (ECM) which upset the constant
track measurement rate. The tracking solution must be
able to handle track period perturbations.
This paper contains the process to select and adjust
the a-p parameters during track initiation and track
perturbations due to mis- or false detections.-This paper considers the theoretical and practical
implementation of the a-p tracking process under the
influence of track rate variations and/or track period
perturbations. These considerations are necessary
when the a-p tracker is used in The Benchmark 96
Target Tracking Problem [l-21, a challenge to the
tracking community to generate tracking solutions to
for a set of maneuvering targets under the influence of
False Alarms (FA), Mis-Detections (MD) and Electronic
Counter-Measures (ECM) which upset the constant
track measurement rate. The tracking solution must be
able to handle track period perturbations.
This paper contains the process to select and adjust
the a-p parameters during track initiation and track
perturbations due to mis- or false detections.
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Author: aida |
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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.
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Author: happy |
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Description: 这是KLT算法论文的高清版。Kanade-Lucas-Tomasi方法，在跟踪方面表现的也不错，尤其在实时计算速度上，用它来得到的，是很多点的轨迹“trajectory”，并且还有一些发生了漂移的点，所以，得到跟踪点之后要进行一些后期的处理，说到Kanade-Lucas-Tomasi方法，首先要追溯到Kanade-Lucas两人在上世纪80年代发表的paper：An Iterative Image Registration Technique with an Application to Stereo Vision，这里讲的是一种图像点定位的方法，即图像的局部匹配，将图像匹配问题，从传统的滑动窗口搜索方法变为一个求解偏移量d的过程，后来Jianbo Shi和Carlo Tomasi两人发表了一篇CVPR(94 )的文章Good Features To Track，这篇文章，主要就是讲，在求解d的过程中，哪些情况下可以保证一定能够得到d的解，这些情况的点有什么特点（后来会发现，很多时候都是寻找的角点）。-KLT is an implementation, in the C programming language, of a feature tracker for the computer vision community. The source code is in the public domain, available for both commercial and non-commerical use.
The tracker is based on the early work of Lucas and Kanade [1], was developed fully by Tomasi and Kanade [2], and was explained clearly in the paper by Shi and Tomasi [3]. Later, Tomasi proposed a slight modification which makes the computation symmetric with respect to the two images-- the resulting equation is derived in the unpublished note by myself [4]. Briefly, good features are located by examining the minimum eigenvalue of each 2 by 2 gradient matrix, and features are tracked using a Newton-Raphson method of minimizing the difference between the two windows. Multiresolution tracking allows for relatively large displacements between images. The affine computation that evaluates the consistency of features between non-consecutive frames [3] was implemented by Thorsten T
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Author: 王凯 |
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Description: Abstract—Several algorithms have been developed for tracking
formant frequency trajectories of speech signals, however most of
these algorithms are either not robust in real-life noise environments
or are not suitable for real-time implementation. The algorithm
presented in this paper obtains formant frequency estimates
voiced segments of continuous speech by using a time-varying
adaptive filterbank to track individual formant frequencies. The
formant tracker incorporates an adaptive voicing detector and a
gender detector for formant extraction continuous speech,
for both male and female speakers. The algorithm has a low signal
delay and provides smooth and accurate estimates for the first four
formant frequencies at moderate and high signal-to-noise ratios.
Thorough testing of the algorithm has shown that it is robust over
a wide range of signal-to-noise ratios for various types of background
noises.-Abstract—Several algorithms have been developed for tracking
formant frequency trajectories of speech signals, however most of
these algorithms are either not robust in real-life noise environments
or are not suitable for real-time implementation. The algorithm
presented in this paper obtains formant frequency estimates
voiced segments of continuous speech by using a time-varying
adaptive filterbank to track individual formant frequencies. The
formant tracker incorporates an adaptive voicing detector and a
gender detector for formant extraction continuous speech,
for both male and female speakers. The algorithm has a low signal
delay and provides smooth and accurate estimates for the first four
formant frequencies at moderate and high signal-to-noise ratios.
Thorough testing of the algorithm has shown that it is robust over
a wide range of signal-to-noise ratios for various types of background
noises.
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Author: assouma |
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Description: Lucas-Kanade 跟踪算法，此算法实现了经典Lk跟踪，详细算法参见文章
An Iterative Image Registration Technique with an Application to Stereo Vision (IJCAI), B.D. Lucas and T. Kanade, 7th International Joint Conference on Artificial Intelligence (IJCAI), April, 1981, pp. 674-679.-Lucus-Kanade Tracker
this implemented the classical Lucas-Kanade Tracker described in the paper:
An Iterative Image Registration Technique with an Application to Stereo Vision (IJCAI), B.D. Lucas and T. Kanade, 7th International Joint Conference on Artificial Intelligence (IJCAI), April, 1981, pp. 674-679.
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Author: ponny213 |
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Description: This paper introduces a new object tracking method
which combines two algorithms working in parallel, and based on
low-level observations (colour and gradient orientation): the Generalised Hough Transform, using a pixel-based description, and
the Particle Filter, using a global description. The object model is
updated by combining information a back-projection map
computed the Generalised Hough Transform, providing
for every pixel the degree to which it may belong to the
object, and the Particle Filter, providing a probability
density on the global object position. The proposed tracker
makes the most of the two algorithms, in terms of robustness to
appearance variation like scaling, rotation, non-rigid deformation
or illumination changes.-This paper introduces a new object tracking method
which combines two algorithms working in parallel, and based on
low-level observations (colour and gradient orientation): the Generalised Hough Transform, using a pixel-based description, and
the Particle Filter, using a global description. The object model is
updated by combining information a back-projection map
computed the Generalised Hough Transform, providing
for every pixel the degree to which it may belong to the
object, and the Particle Filter, providing a probability
density on the global object position. The proposed tracker
makes the most of the two algorithms, in terms of robustness to
appearance variation like scaling, rotation, non-rigid deformation
or illumination changes.
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Author: SALEH |
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Description: 17年cvpr上的目标跟踪方法CSR-DCF的matlab源码(Matlab implementation of the DCF-CSR tracker from the paper published in the proceedings of Conference on Computer Vision and Pattern Recognition (CVPR) 2017.)
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Author: shaojia
|
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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.
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Size: 7226368 |
Author: 大大大大大澈儿 |
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