Description: kalman是视频跟踪的几本算法，本算法就是一个kalman的很好的应用例子！-video tracking kalman several algorithms, this algorithm is an application of kalman good example! Platform: |
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Author:赵佳 |
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Description: 基于opencv的SIFT图像特征跟踪程序，应用于视频序列中跟踪图像特征-Opencv-based SIFT feature tracking program, used to track image features in video sequences Platform: |
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Author:李涵露 |
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Description: 《DragonEye Fast Object Tracking and
Camera Motion Estimation》DragonEye是一个基于目标直接浏览的视频播放系统。比Dimp和Dragon来说具有实时性-Recently, multiple research groups proposed the concept of direct manipulation,
which makes the task of navigation in videos directly related to content of the
video. However, the developed systems are still too slow for a broad application of
the technique. To increase the accessibility of direct manipulation a system has to
fulfill certain requirements:
• Computation times have to be so small that the system can work in situation
with time constraints.
• The system has to accommodate for the user’s greater comprehension of a
scene. This includes camera motion, fore- and background, and occlusions.
Based on the requirements, we developed and implemented the DRAGONEYE system.
The tracker employs point tracking, with points provided by SIFT [Lowe,
2004], and color tracking with the CAMShift algorithm [Comaniciu et al., 2003]. A
model of the tracked object is built and continuously updated to fit the current conditions.
This model allows the detection and recover Platform: |
Size: 38917120 |
Author:童癸 |
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Description: 针对不同摄像头的监控视频序列, 提出了一种基于视频帧 SIFT ( Scale Invariant Feature Transform, 即尺度不变特征变换)
特征跟踪的拼接方法。通过 SIFT算法提取帧图像的特征, 并在跟踪的估计区域搜索匹配特征, 从而确定待整合帧之间的变换参数。实验结果表明, 该方法较好实现视频快速拼接, 且对重叠区域小、 形变大、 有运动物体遮挡的视频具有较强的鲁棒性。-Surveillance cameras for different video sequences, a video frame based on SIFT (Scale Invariant Feature Transform, that is scale invariant feature transform) feature tracking on the method. Frame by extracting SIFT features algorithm, and search tracking matching characteristics of the estimated area to be integrated to determine the transformation parameters between frames. Experimental results show that the method is better for video fast stitching, and on the overlap is small, deformation, and have a block of video with moving objects is robust. Platform: |
Size: 107520 |
Author:fenghao |
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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: |
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Author: |
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Description: SIFT特征（Scale-invariant feature transform，尺度不变特征转换)是一种电脑视觉的算法用来侦测与描述影像中的局部性特征，它在空间尺度中寻找极值点，并提取出其位置、尺度、旋转不变量，此算法由 David Lowe 在1999年所发表，2004年完善总结。其应用范围包含物体辨识、机器人地图感知与导航、影像缝合、3D模型建立、手势辨识、影像追踪和动作比对。-Scale-invariant feature transform (or SIFT) is an algorithm in computer vision to detect and describe local features in images. The algorithm was published by David Lowe in 1999.[1]
Applications include object recognition, robotic mapping and navigation, image stitching, 3D modeling, gesture recognition, video tracking, and match moving. Platform: |
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Author:张博 |
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Description: Scale-invariant feature transform (or SIFT) is an algorithm in computer vision to detect and describe local features in images. The algorithm was published by David Lowe in 1999.[1]
Applications include object recognition, robotic mapping and navigation, image stitching, 3D modeling, gesture recognition, video tracking, individual identification of wildlife and match moving.
The algorithm is patented in the US the owner is the University of British Columbia.-Scale-invariant feature transform (or SIFT) is an algorithm in computer vision to detect and describe local features in images. The algorithm was published by David Lowe in 1999.[1]
Applications include object recognition, robotic mapping and navigation, image stitching, 3D modeling, gesture recognition, video tracking, individual identification of wildlife and match moving.
The algorithm is patented in the US the owner is the University of British Columbia. Platform: |
Size: 90112 |
Author:dhivya |
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Search - video sift tracking