Description: Most motion-based tracking algorithms assume that objects undergo rigid motion, which is most likely disobeyed in real world. In this paper, we present a novel motion-based tracking framework which makes no such assumptions. Object is represented by a set of local invariant features, whose motions are observed by a feature correspon-
dence process. A generative model is proposed to depict
the relationship between local feature motions and object
global motion, whose parameters are learned efciently by
an on-line EM algorithm. And the object global motion is estimated in term of maximum likelihood of observations.Then an updating mechanism is employed to adapt object representation. Experiments show that our framework is
exible and robust in dealing with appearance changes,background clutter, illumination changes and occlusion Platform: |
Size: 424705 |
Author:chenjieke |
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Description: 学位论文；运动物体跟踪方法主要包括卡尔曼滤波，Mean-shift，Camshifi算法，粒子滤波器，Snake模型等；应用卡尔曼滤波方法设计了一套煤矿矿工出入自动监测系统；提出了一种新的基于高斯混合模型的颜色特征提取方法，该方法克服了现有的Camshift算法Continuousl y Adaptive eanshift中跟踪目标特征提取精确度低和计算复杂度高的缺陷-Dissertation moving object tracking methods include Kalman filtering, Mean-shift, Camshifi algorithm, particle filter, Snake model the application of Kalman filtering method designed a coal miners out of automatic monitoring system a new Gaussian mixture model based on the color feature extraction method to overcome the existing Camshift algorithm Continuousl y Adaptive eanshift track target feature extraction accuracy and low computational complexity and high defects Platform: |
Size: 1343488 |
Author:田卉 |
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Description: 基于投票算法的目标跟踪,基于二阶非线性投票的多目标跟踪算法。该算法通过目标匹配得到同一目标在不同帧中的位置，同时利用特征监测来处理目标的遮挡、分裂问题，并实现目标特征的实时更新。在目标匹配过程中，通过对目标前一帧与当前帧的特征相似性进行投票，得到匹配目标。利用视频图像进行实验，结果表明：该方法对噪声、阴影、遮挡、分裂等具有良好的鲁棒性，较好地实现了多目标的跟踪。-The method used object matching to get objects’ position in different frames, and used feature monitoring to deal with object occlusion, object split and implement real-time update for objects features. Objects were matched based on the similarity voting of their features in successive frames. Experimental study had been carried out using image sequences captured in real scene. The experimental results show that the method is robust against noise, shadows, occlusion, and split and it performs multiple objects tracking finely. Platform: |
Size: 1024 |
Author:崔瑞芳 |
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Description: This library implements the KLT Tracking algorithm [2004] for Feature Tracking in Video useful in computer vision tasks like object recognition, image indexing, tracking and structure from motion. This implementation uses programmable Graphics Hardware to achieve considerable speedup in the running time of the GPU-based implementation. Platform: |
Size: 49152 |
Author:Alex |
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Description: This project deals with the tracking and following of single object in a sequence of frames
and the velocity of the object is determined. Algorithms are developed for improving
the image quality, segmentation, feature extraction and for deterring the velocity. The
developed algorithms are implemented and evaluated on TMS320C6416T DSP Starter
Kit (DSK). Segmentation is performed to detect the object after reducing the noise from
that scene. The object is tracked by plotting a rectangular bounding box around it in
each frame. The velocity of the object is determined by calculating the distance that the
object moved in a sequence of frames with respect to the frame rate that the video is
recorded. The algorithms developed can also be used for other applications (real time,
object classi cation, etc.). Platform: |
Size: 1197056 |
Author:vikas |
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Description: 该源码是关于运动对象跟踪的算法，主要实现了高斯背景建模，全局运动补偿（SIFT特征和RANSAC算法），运动对象检测，对象跟踪算法（Mean Shift，Particle Filter等），对象特征提取（轨迹，大小，起止帧等），同时，程序基于VC2008+OpenCV开发，实现了对话框式的程序界面，效率高。-This is a source about motion object tracking, including foreground modeling,object detection,object tracking,feature generation. It developed based on VC2008 and OpenCV library. The friendly interface is very convinient for new learners. Platform: |
Size: 7499776 |
Author: |
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Description: 本系统中VIS欠缺的SIFT_VC.lib文件。。。
http://www.pudn.com/downloads224/sourcecode/math/detail1055031.html-This is lib file, which is used in Video Intelligent System (VIS) based on the Microsoft Visual Studio 2008 compiler environment and OpenCV 2.0 library. It includes foreground detection, motion object detection, motion object tracking, trajectories generation and analysis modules. It realizes a friendly interface based on dialog, which provides a convenient example for new learners.
keywords: opencv, mixture of gaussian model, sift feature and ransac method, mean shift, particle filter, kalman filter, object detection and tracking, video intelligent system. Platform: |
Size: 111616 |
Author: |
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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: 室外场景下由于场景背景条件变化容易导致视频目标跟踪稳定性差。该文提出一种利用红外和可见光传感
器的双通道视频目标跟踪方法。该算法利用可见光图像的目标颜色特征和红外图像的目标轮廓特征，结合均值漂移
算法与水平集曲线演化实现目标定位，并给出了目标尺度和模板更新方法；对多目标跟踪的互相遮挡问题，通过判
断目标合并与分离实现遮挡时多个目标的定位。实验结果表明，该文方法能够有效处理光照变化、阴影、遮挡等情
况，实现目标的稳定跟踪。-Considering the poor stability of the video object tracking methods in outdoor scenes when the
background circumstance variation in object images occurs, a new method is presented for object tracking based on
infrared and visible dual-channel video. It extracts the Hue, Saturation, Value (HSV) color feature in visible image
and the contour feature in infrared image, and combines the Mean Shift (MS) algorithm and the level set evolution
algorithm to realize object location, also, the object scalar and model update mechanism is presented. To address
the multiple object occlusion problems, a method is presented to locate multiple objects by determining the object
merger and separation. Experimental results on infrared and visible dual-channel video demonstrate that the
proposed method can successfully cope with the cases in complex environment such as illumination changes,
shadow, occlusion, etc.. Platform: |
Size: 386048 |
Author:majun |
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Description: 基于选定区域颜色信息的跟踪算法，通过鼠标选择区域，计算区域颜色特征，对物体进行跟踪-Based on the selected regions of color information in the tracking algorithm, through the mouse to select the area, calculation region color feature, object tracking Platform: |
Size: 3072 |
Author:cleverboy |
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Description: We present a parallel implementation of a histogram-based particle filter for object tracking on smart
cameras based on SIMD processors. We specifically focus on parallel computation of the particle weights
and parallel construction of the feature histograms since these are the major bottlenecks in standard
implementations of histogram-based particle filters. The proposed algorithm can be applied with any histogram-
based feature sets—we show in detail how the parallel particle filter can employ simple color histograms
as well as more complex histograms of oriented gradients (HOG). The algorithm was successfully
implemented on an SIMD processor and performs robust object tracking at up to 30 frames per second—a
performance difficult to achieve even on a modern desktop computer. Platform: |
Size: 7321600 |
Author:gugu |
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Description: 本文提出一种通过实时调整目标特征权值来进行背景自适应跟踪的算法。首先，定义了一种综合特征集合用以描述目标的颜色和局部轮廓。其次，提出了在滤波框架中对目标特征进行评估的算法，从而使得具有强区分能力的特征占有较大的权值，进而使其能够在跟踪过程起到较大的作用。采用传统的Kalman 滤波和粒子滤波对所提出的算法进行了验证。-In this paper, we propose a new adaptive visual object tracking method based on
online feature evaluation approach. First, a feature set is built by combining color
histogram (HC) with gradient orientation histogram (HOG), which emphasizes both
color and contour representation. Then a feature confidence evaluation approach is
proposed to make features with higher confidences play more important roles in the
instantaneous tracking ensuring that the tracking can adapt to the appearance change
of both the object and its background. The feature evaluation approach is fused with
filter frameworks, e.g. Kalman and Particle filter, to keep the temporal consistency of feature confidence evolution. Platform: |
Size: 1602560 |
Author:wenping |
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Description: 计算机视觉研究的主要问题之一是运动物体的检测与跟踪, 它将图像处理、模式识别、自动控制、人
工智能和计算机等很多领域的先进技术结合在了一起, 主要应用在军事视觉制导、视频监控、医疗诊断和智能交通
等各个方面, 因此该技术已经成为一个重要的研究方向。阐述了视觉跟踪算法的研究现状和视觉跟踪算法的种类,
研究了基于区域的跟踪算法、基于模型的跟踪算法、基于特征的跟踪算法和基于主动轮廓的跟踪算法, 探讨了视觉
跟踪算法的未来研究方向。-One of the computer vision tasks is moving object detection and tracking, it combines the advanced technologies together in
many fields such as image processing, pattern recognition, automatic control, artificial intelligence, computer, etc. It is mainly used in
such aspects as military visual guidance, video monitoring, medical diagnosis, intelligent transportation, etc. Object tracking technology has
been used in many important areas, so it has become a major direction of computer vision research. The state of research in visual tracking
algorithms and the common visual tracking algorithms are discussed in this paper. The region based tracking algorithms, active contour based
tracking algorithms, feature based tracking algorithms and model based tracking algorithms are studied. The future research direction for
vision tracking algorithm is discussed. Platform: |
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Author:cp |
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Description: This paper presents a feature-based object tracking algorithm using optical flow under the non-prior training (NPT) active feature
model (AFM) framework. The proposed tracking procedure can be divided into three steps: (i) localization of an object-of-interest,
(ii) prediction and correction of the object’s position by utilizing spatio-temporal information, and (iii) restoration of occlusion using
NPT-AFM. Platform: |
Size: 2194432 |
Author:Felix |
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Description: 研究了目前运动对象检测与跟踪的一些常用方法,包括时域差分法、背景差分法、基于光流场的检测方法和卡尔曼滤波、特征光流法的跟踪方法,并对各种方法进行了比较,指出其优缺点及适用范围,并给出了时域差分及背景差分方法的实验结果-Currently some of the commonly used methods to study the detection and tracking of moving objects, including difference time domain method, background subtraction method, optical flow detection and tracking method Kalman filtering, feature-based optical flow method, and various methods of comparison, pointing out its strengths and weaknesses and the scope of application, and the experimental results of differential and background difference time-domain method Platform: |
Size: 249856 |
Author:hengluo |
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Description: Color can provide an efficient visual feature for tracking nonrigid
objects in real-time. However, the color of an object can vary over
time dependent on the illumination, the visual angle and the camera
parameters. To handle these appearance changes a color-based target
model must be adapted during temporally stable image observations.
This paper presents the integration of color distributions into particle
filtering and shows how these distributions can be adapted over time. A
particle filter tracks several hypotheses simultaneously and weights them
according to their similarity to the target model. As similarity measure
between two color distributions the popular Bhattacharyya coefficient is
applied. In order to update the target model to slowly varying image
conditions, frames where the object is occluded or too noisy must be
discarded. Platform: |
Size: 226304 |
Author:yangs |
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Search - feature-based object tracking