Description: 目标：手工选择视频图像上的待跟踪目标；利用块匹配的方法估计目标区域在下一帧图像中的位置；循环这个过程直到目标从图像帧中消失。
技术：avi视频流的帧读取；图像帧存储格式的了解；RGB图像的灰度化；灰度图象的平滑滤波；块运动估计算法的实现；使用MFC显示图像帧；MFC上鼠标事件的使用。
-objectives : manually select video images on the question target tracking; Use block matching methods for estimating the target region in the next frame image position; Until this process cycle from the goal frame images disappear. Technology : avi video streaming reading frame; Image frame storage formats; RGB images of gray; Gray image smoothing filter; Block motion estimation algorithm implementation; MFC showed images frame; MFC on the use of the mouse. Platform: |
Size: 110080 |
Author:张 |
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Description: 目标：手工选择视频图像上的待跟踪目标；利用块匹配的方法估计目标区域在下一帧图像中的位置；循环这个过程直到目标从图像帧中消失。
技术：avi视频流的帧读取；图像帧存储格式的了解；RGB图像的灰度化；灰度图象的平滑滤波；块运动估计算法的实现；使用MFC显示图像帧；MFC上鼠标事件的使用。
-objectives : manually select video images on the question target tracking; Use block matching methods for estimating the target region in the next frame image position; Until this process cycle from the goal frame images disappear. Technology : avi video streaming reading frame; Image frame storage formats; RGB images of gray; Gray image smoothing filter; Block motion estimation algorithm implementation; MFC showed images frame; MFC on the use of the mouse. Platform: |
Size: 110592 |
Author:张 |
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Description: 一个很好的基于经典粒子滤波和均值漂移的混合算法，在选定目标初始位置的情况下能很有效的跟踪目标-A good particle filter based on the classic mean-shift and the hybrid algorithm, the target initial position in selected cases can be very effective in tracking the target Platform: |
Size: 176128 |
Author:张浩天 |
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Description: 1、 假定有一目标沿水平方向运动，起始位置为(-2000米，500米)，运动速度为10米/秒，扫描周期 秒， , 米，采用蒙特卡洛方法对跟踪滤波器进行仿真分析，仿真次数为100次-1, it is assumed that the target has one movement along the horizontal direction, the starting position for the (-2000 meters, 500 meters), velocity of 10 m/s, scanning cycle of seconds, and rice, using the Monte Carlo method for simulation analysis of tracking filter The simulation frequency was 100 times Platform: |
Size: 124928 |
Author:刘思 |
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Description: 提出了装配机器人系统中一种基于视觉引导和超声测距的运动目标跟踪和抓取
方法。介绍了运动目标的跟踪原理，采用图像雅可比矩阵进行机器人运动控制，跟踪运动
的目标。利用视觉引导技术，获取目标在图像平面中的位置与方位，进行平面跟踪，然后
引导超声波测距装置测取目标深度信息。实验结果验证了本文提出的这种方法的可行性
和有效性。-A robot assembly system based on visual guidance and Ultrasonic Ranging Moving Target Tracking and crawling methods. Introduce a moving target tracking principle, the use of image Jacobian matrix for robot motion control, track campaign goals. Guide the use of visual technology, access to the target in the image plane position and orientation for flat track, and then guide the ultrasonic ranging device target measured depth information. The experimental results verify the proposed method is feasible and effective. Platform: |
Size: 249856 |
Author:高子 |
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Description: Tracking targets with radar is an important step in ensuring safety in such endeavors as air travel or military operations. To account for inherent inaccuracies in raw radar measurements of position, and to obtain accurate velocity data, we implemented an algorithm called the Kalman Filter. The resulting track data was used, in conjunction with algebraic and trigonometric methods, to simulate target interception and collision prevention. Our system led an interceptor aircraft to its target and warned pilots of a potential collision, proving the effectiveness of our filter. Platform: |
Size: 320512 |
Author:Nadir |
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Description: 统的联合概率数据关联算法(IPDA)是在密集杂波环境下的一种良好的多目标跟踪算法，
但它是针对单传感嚣对多目标跟踪的情况下使用，不能直接用于多传感器对多目标的跟踪。针对这
一问题，文中提出了一种适用于多传感器多目标跟踪的JPDA算法，它以极大似然估计完成对来自
多传感器的测量集合进行同源最优分划，然后采用JPDA方法对多目标进行跟踪。经过理论分析和
仿真试验，证明了该方法能有效地进行多传感器多目标的跟踪，且具有算法简单、跟踪精度高、附
加的计算量小等优点。-Abstract)m JointProbabilisticDataAssociation(JPDA)solves single sensormultitatgcttrackingin ClUtter,butitc锄not bc
used directly in mnitisensor multitarget tracking．喇8 paper presents a method to implement multi—sensor multi—target tracking
by combining maximum likelihood estimation wim JPDA The maximum Iikelihood estimation is used to classify the same
source observations at one time into the same set,and血en JPDA implements multi—target uacking after the position fitsed．The
theory analysis and computer simulation show that this algorithm may achieve multi—sensor multi—target tracking perfectly with
low calculation load added and higher precision 。 Platform: |
Size: 246784 |
Author:minnie |
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Description: ：针对目标跟踪过程中目标尺度伸缩和姿态形状的变化引起的目标丢失，以及使用单个模型跟踪
机动目标不够理想，提出一种基于SIFT特征的自适应滤波目标跟踪算法。仿真结果表明，该算法在
目标机动时，跟踪性能远优于其它特征匹配算法和多模型算法，而且计算量小，能保证跟踪的实时性。-Abstract：Aim at the lose of target due to scale。invarl’ant，position change and deformation，as welI as
dissatisfied single model tracking in the process of target tracking．an adaptive algorithm based on SIFT is
proposed．Simulation results show that tracking perform ance of method in this paper is far better than other
feature matching and multiple-model algorithms．Furtherm ore，the computational load of the proposed
method is less，and can ensure the real—time perform ance in tracking Platform: |
Size: 168960 |
Author:陈方芳 |
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Description: 在基于Mean-Shift的目标跟踪算法中，尺度自适应机制是算法研究的一个重要方向。一种典型的方法采
用Lindeberg的尺度空间理论以获取目标尺度信息。但现有算法中将尺度由2．D矢量压缩为1一D量，未能精细地
刻画目标仿射变换时的尺度变化，从而限制了算法的适用范围。为此，该文将尺度维1．D滤波推广至2一D，构造得
到了相应的垂D尺度空间，并利用窄间维和尺度维的Mean-Shift交替迭代，实现了同时在空间位置和尺度方向对
目标的有效跟踪，提高了算法在目标尺度变化时的自适应性，并扩大了算法的适用范围。
关键-Scale adaptive mechanism in the target tracking algorithm based on Mean-Shift algorithm is an important direction of research. A typical method using Lindeberg' s scale space theory to obtain the target scale. However, the existing algorithms will scale from 2. D vector compression for 1-D volume, not fine to portray the target affine transformation of scale changes, thus limiting the scope of application of the algorithm. To this end, the paper will scale dimension. D filter to the 2-D structure has been the vertical D scale space and the use of narrow inter-dimension and scale dimension of the Mean-Shift alternating iteration, effective tracking of the target position in space and scale direction to improve the self-adaptive algorithm when the target scale changes, and to expand the scope of application of the algorithm. Key Platform: |
Size: 536576 |
Author:张娜 |
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Description: 用Kalman滤波方法对单个目标航迹进行预测，并借助于Matlab仿真工具，对实验的效果进行评估。
-Target tracking application context radar data processing, radar in the search to the goals and objectives of the record location data of the measured target position data (called a point of the track) processing, automatic formation flight path, and the target in the next The moment the position prediction. This article briefly discusses the Kalman filtering method to predict a single target track, and by means of Matlab simulation tools to assess the effect of the experimental. Platform: |
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Author:徐天宇 |
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Description: 具有实时检测、跟踪和分析判断的智能化监控系统是智能化监控系统发展的必然趋势，本文提出一种在实时监控下检测和跟踪运动目标的方法，首先使用背景差分法检测出运动目标，并定期使用背景更新策略对参考背景进行局部更新，这样可以提高目标检测的精确度。目标跟踪时在不同的尺度空间获得目标的关键点，增强算法目标在遮挡情况下的鲁棒性， 接下来使用mean shift 算法估计目标在下一帧的位置。-Intelligent monitoring system with real-time detection, tracking and analysis to determine is the inevitable trend of development of the intelligent monitoring system, this paper presents a real-time monitoring, detection and tracking of a moving target, and the first to use background subtraction method to detect moving targets, and The regular use of background updating strategy partial update of the reference background, so you can improve the accuracy of target detection. Target tracking target key points in different scale space, enhance the robustness of the algorithm target under occlusion, followed by use of the mean shift algorithm to estimate the target position in the next frame. Platform: |
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Author:yaomeng |
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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 |
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Description: 本文针对摄像机运动情况下的多目标跟踪问题，提出了基于粒子滤波的跟踪算法。在粒子滤波算法基础上，将二阶自回归过程作为系统状态转移模型，HSV 颜色直方图作为观测模型，对视频中多个目标的位置、大小进行跟踪。实验结果表明，该算法能实时正确地跟踪多个目标，并对局部遮挡有较好的鲁棒性，也能在目标短暂消失导致跟踪失败后，在目标重新出现后及时捕获并继续进行跟踪。-A tracking algorithm based on particle filter is proposed to deal with multi-target tracking in a camera motion situation. In the
proposed algorithm, the position and scale of targets in the video are tracked by using a second-order auto-regressive process as the transition
model and an HSV histogram as the observation model. Experimental results show that the proposed algorithm can track multitarget
correctly in real-time and is robust to the partial occlusion. In addition, it can recover from tracking failure due to the target out of camera for a while. Platform: |
Size: 405504 |
Author:wenping |
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Description: 下面是利用卡尔曼滤波的方法，实现雷达对目标的跟踪:一目标沿水平方向运动，起始位置为（－2000米，1000米），运动速度为15米/秒，扫描周期T＝2秒， 米，采用蒙特卡洛方法对跟踪滤波器进行仿真，仿真次数为100次。-Below is the use of Kalman filtering method, to achieve the target tracking radar. Parameter :: a target in a horizontal direction, the starting position (2000m, 1000m), the motion speed of 15 m/s, the scan period T = 2 seconds, rice, Monte Carlo method using the tracking filter simulation, simulation 100 times. Platform: |
Size: 4096 |
Author:Allen Wang |
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Description: 最新的视频目标跟踪算法。用于监控视频中自动跟踪目标的位置。-Latest video target tracking algorithm. Used for automatically tracking target s position in video surveillance. Platform: |
Size: 729088 |
Author:水木 |
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Description: 雷达检测跟踪过程无缝连接过程。 雷达检测跟踪过程无缝连接过程。-Detection and tracking are normally considered separate processes. First the signal processing system associated with a sensor examines the signal to determine whether to call detection. Once detection is called, it is converted to an estimate of one or more of the components of the target s kinematic state, e.g., bearing, position, or velocity. This estimate (contact) is sent to a tracking system that determines whether the contact should be associated with an existing track or used to generate a new one. This process works well in high signal-to-noise ratio (SNR) situations but sacrifices performance in low ones. The tracking community is making progress toward seamless detection and tracking and recovering some of this lost performance. Platform: |
Size: 153600 |
Author:Haiser |
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Description: 基于Opencv的视频序列前景检测模块，可以用来检测自动化目标跟踪的初始位置-Opencv based video sequences foreground detection module,can be used to detect the initial position in automated target tracking Platform: |
Size: 19801088 |
Author:Elaine |
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Description: 本程序在图像上指定位置话红色的矩形框作为标记，用在目标跟踪上很有用。-This procedure is specified, then the position of the red rectangle on an image as a mark for use in target tracking useful. Platform: |
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Author:戴知圣 |
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Description: The estimation of target position and velocity is attempted on the basis of angular measurements only. This topic is of
interest for: airborne radar and sonar in passive listening mode, and electronic warfare systems in these cases the target
radiates either an electro-magnetic (e.m.) or an acoustic wave. The measurements are a!ected by white Gaussian noise
the presence of outliers is also considered. Two types of tracking lters are presented: (i) one processes a batch of data, (ii)
the other recursively processes the data-The estimation of target position and velocity is attempted on the basis of angular measurements only. This topic is of
interest for: airborne radar and sonar in passive listening mode, and electronic warfare systems in these cases the target
radiates either an electro-magnetic (e.m.) or an acoustic wave. The measurements are a!ected by white Gaussian noise
the presence of outliers is also considered. Two types of tracking lters are presented: (i) one processes a batch of data, (ii)
the other recursively processes the data Platform: |
Size: 286720 |
Author:Gomaa Haroun |
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Description: 功能说明： UKF在目标跟踪中的应用
参数说明： 1、状态6维，x方向的位置、速度、加速度；y方向的位置、速度、加速度； 2、观测信息为距离和角度；
详细原理介绍及中文注释请参考：《卡尔曼滤波原理及应用-MATLAB仿真》，电子工业出版社，黄小平著。(Function Description: application of UKF in target tracking
Parameter Description: 1, state 6 dimension, position, velocity, acceleration of X direction, position, velocity and acceleration of Y direction; 2, observation information is distance and angle;
Details of the principle and Chinese notes please refer to: "Calman filtering principle and application of -MATLAB simulation", electronic industry press, Huang Xiaoping.) Platform: |
Size: 1024 |
Author:lzsgdtc
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Search - position in target tracking