Description: There are many approaches for motion detection in continuous video streams. All of them are based on comparison of current video frame with one from the previous frames or with something that we ll call background. In this article I ll try to describe some of the most common approaches. In describing the algorithms, I ll use an image processing library I ve described in my previous article. So, if you are common with it, it will only help. The demo applications support the following types of video sources: AVI files (using Video for Windows, interop library is included) updating JPEG from internet cameras MJPEG (motion JPEG) streams from different internet cameras MMS Stream - Microsoft Media Services local capture device (USB cameras or other capture devices). Platform: |
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Author:北悠 |
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Description: There are many approaches for motion detection in continuous video streams. All of them are based on comparison of current video frame with one from the previous frames or with something that we ll call background. In this article I ll try to describe some of the most common approaches. In describing the algorithms, I ll use an image processing library I ve described in my previous article. So, if you are common with it, it will only help. The demo applications support the following types of video sources: AVI files (using Video for Windows, interop library is included) updating JPEG from internet cameras MJPEG (motion JPEG) streams from different internet cameras MMS Stream - Microsoft Media Services local capture device (USB cameras or other capture devices).-There are many approaches for motion detection in continuous video streams. All of them are based on comparison of current video frame with one from the previous frames or with something that we ll call background. In this article I ll try to describe some of the most common approaches. In describing the algorithms, I ll use an image processing library I ve described in my previous article. So, if you are common with it, it will only help. The demo applications support the following types of video sources: AVI files (using Video for Windows, interop library is included) updating JPEG from internet cameras MJPEG (motion JPEG) streams from different internet cameras MMS Stream- Microsoft Media Services local capture device (USB cameras or other capture devices). Platform: |
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Author: |
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Description: 外国人写的在固定背景下检测并跟踪目标的算法，并加入了快速的背景更新算法，利于Kalman滤波来预测目标位置-Written by foreigners in the context of a fixed target detection and tracking algorithms, and joined the fast background update algorithm, in favor of Kalman filtering to predict the target location Platform: |
Size: 192512 |
Author:lizhen |
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Description: 这本书全面系统地讨论了数字图像处理的理论，设计及应用。全书共分为12章，分别讨论了位图及图像类的概念、图像获取、图像增强、图像复原、正交变换、压缩编码、图像配准、运动检测、特征提取、图像分割及识别的相关知识，基本涵盖了从图像获取到图像处理的各个领域，并给出了相应的算法和源代码。-This book is a comprehensive and systematic discussion of the theory of digital image processing, design and application. Book is divided into 12 chapters, respectively, discussed the types of digital maps and images of the concept, image acquisition, image enhancement, image restoration, orthogonal transform, compression, image registration, motion detection, feature extraction, image segmentation and identification of the relevant knowledge, basic cover from image acquisition to image processing in various fields, and the corresponding algorithms and source code. Platform: |
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Author:liuzhiriu |
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Description: 人体运动检测与运动跟踪的源代码，用到了最新的算法，对这方面研究的人员很有帮助-Human Motion Detection and Motion Tracking source code, use the latest algorithms, this study helpful staff Platform: |
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Author:郝晓 |
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Description: 《Visual C++数字图像获取 处理及实践应用》配套源程序
本书全面系统地讨论了数字图像处理的理论、设计及应用。全书由自成体系而又互有联系的12章组成，分别讨论了位图及图像类的概念、图像获取、图像增强、图像复原、正交变换、压缩编码、图像配准、运动检测、特征提取、图像分割及识别的相关知识，基本涵概了从图像获取到图像处理的各个领域，并结合Microsoft公司面向对象的可视化集成编程系统Visual C++，给出了相应的算法和完整的源代码。- Visual C++ Digital image acquisition processing and practical application of complementary source book comprehensive and systematic discussion of the theory of digital image processing, design and application. Book by the self-and inter-linked to the composition of Chapter 12, respectively, discussed the types of digital maps and images of the concept, image acquisition, image enhancement, image restoration, orthogonal transform, compression, image registration, motion detection, feature extraction, image segmentation and identification of related knowledge, basic robust from the image acquisition to image processing in various fields and, in conjunction with Microsoft Corporation Visual object-oriented programming system integrated Visual C++, the corresponding algorithms and the complete source code. Platform: |
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Author:ss |
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Description: 运动目标提取和跟踪是视觉分析、高级行为理解以及运动编码等计算机视觉
应用领域中基本而又重要的一步，也是计算机视觉中的难点问题。本文深入研究
了运动视觉中目标的精确提取与跟踪算法，包括运动检测、阴影消除、外轮廓提
取以及目标的跟踪四个方面-Extraction and tracking of moving targets is a visual analysis, to understand high-level acts, as well as encoding movement in computer vision applications, such as basic and important step, but also in computer vision problems. In this paper, the movement in-depth study of the accuracy of visual target extraction and tracking algorithms, including motion detection, the shadow of the elimination of external contour extraction and tracking of targets in four areas Platform: |
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Author:丁行 |
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Description: 采用 CAMSHIFT 算法快速跟踪和检测运动目标的 C/C++ 源代码，OPENCV BETA 4.0 版本在其 SAMPLE 中给出了这个例子。算法的简单描述如下-This application demonstrates a fast, simple color tracking algorithm that can be used to track faces, hands . The CAMSHIFT algorithm is a modification of the Meanshift algorithm which is a robust statistical method of finding the mode (top) of a probability distribution. Both CAMSHIFT and Meanshift algorithms exist in the library. While it is a very fast and simple method of tracking, because CAMSHIFT tracks the center and size of the probability distribution of an object, it is only as good as the probability distribution that you produce for the object. Typically the probability distribution is derived from color via a histogram, although it could be produced from correlation, recognition scores or bolstered by frame differencing or motion detection schemes, or joint probabilities of different colors/motions etc.
In this application, we use only the most simplistic approach: A 1-D Hue histogram is sampled from the object in an HSV color space version of the image. To produce the Platform: |
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Author:黄文伟 |
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Description: 本文实现了在连续视频数据流中几种不同的运动检测算法，他们都是基于当前帧图像和前一帧图像的比较，程序使用了AForge.NET framework库。其中的示例代码支持下面几种视频格式：AVI文件、网路相机的JPEG和MJPEG，本地的采集设备(USB相机等)。-This article implements a continuous video data stream in several different motion detection algorithms, they are based on the current frame and previous frame image comparison, the program uses the AForge.NET framework library. Sample code which supports the following video format: AVI file, network camera JPEG and MJPEG, the local collection devices (USB cameras, etc.). Platform: |
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Author:憨豆 |
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Description: Motion Detection Algorithms
There are many approaches for motion detection in a continuous video stream. All of them are based on comparing of the current video frame with one from the previous frames or with something that we ll call background. In this article, I ll try to describe some of the most common approaches.
In description of these algorithms I ll use the AForge.NET framework, which is described in some other articles on Code Project: [1], [2]. So, if you are common with it, it will only help.
The demo application supports the following types of video sources:
• AVI files (using Video for Windows, interop library is included)
• updating JPEG from internet cameras
• MJPEG (motion JPEG) streams from different internet cameras
• local capture device (USB cameras or other capture devices, DirectShow interop library is included). -Motion Detection Algorithms
There are many approaches for motion detection in a continuous video stream. All of them are based on comparing of the current video frame with one from the previous frames or with something that we ll call background. In this article, I ll try to describe some of the most common approaches.
In description of these algorithms I ll use the AForge.NET framework, which is described in some other articles on Code Project: [1], [2]. So, if you are common with it, it will only help.
The demo application supports the following types of video sources:
• AVI files (using Video for Windows, interop library is included)
• updating JPEG from internet cameras
• MJPEG (motion JPEG) streams from different internet cameras
• local capture device (USB cameras or other capture devices, DirectShow interop library is included). Platform: |
Size: 282624 |
Author:reyjav |
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Description: In this thesis we present an operational computer vision system for real-time detection and tracking of human motion. The system captures monocular video of a scene and identifies those moving objects which are characteristically human. This serves as both a proof-ofconcept and a verification of other existing algorithms for human motion detection. An approach to statistical modeling of motion developed by Y. Song is coupled with a preprocessing stage of image segmentation and point feature tracking. This design allows a system that is robust with respect to occlusion, clutter, and extraneous motion. The results of experiments with the system indicate the ability to minimize both false detections and missed detections. Platform: |
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Author:linuszhao |
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Description: Face detection algorithms are widely used in computer vision as they provide fast and reliable results depending on the application
domain. A multi view approach is here presented to detect frontal and profile pose of people face using Histogram of Oriented Gradients, i.e. HOG, features. A K-mean clustering technique is used in a cascade of HOG feature classifiers to detect faces. The evaluation of the algorithm shows similar performance in terms of detection rate as state of the art algorithms. Moreover, unlike state of the art algorithms,our system can be quickly trained before detection is possible. Performance is considerably increased in terms of lower computational cost and lower false detection rate when combined with motion constraint given by moving objects in video sequences. The detected HOG features are integrated within a tracking framework and allow reliable face tracking results in several tested surveillance video sequences. Platform: |
Size: 293888 |
Author:linuszhao |
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