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- 采用 CAMSHIFT 算法快速跟踪和检测运动目标的 C/C++ 源代码-algorithm used CAMSHIFT rapid detection and tracking of moving objects in C / C source code
CamShift_opencv
- CamShift求運\動輪廓，使用opencv 和 c
camshift.rar
- camshift 摄像头跟踪人脸技术 可以用于人脸识别和人机交互,great！
CamShift
- 基于OpenCV的视频目标跟踪源码。输入的可以是视频文件或摄像头采集。-OpenCV canny a subject.
VideoTest
- 基于opencv的视频播放器 主要实现功能 提取视频 光流 和颜色直方图 并存储起来用于分析视频-Opencv-based video player to achieve the main function of extracting the optical flow and color histogram of the video and stored video for analysis
camshift
- 冲文件中读取视频，利用camshift对彩色咪表进行跟踪-Red paper to read the video, use the color meter track camshift
trackingbyMFC
- 通过vc++的MFC编写的一个智能的视频跟踪系统，采用了camshift算法和MFC结合，从摄像头读取数据，能够控制跟踪和保存图像等功能，性能相当不错！-By vc++ for MFC written in an intelligent video tracking system, using algorithms and MFC camshift combination of read data from a camera that can track and save the image c
facedetect
- 用摄像头采集人脸图像并进行识别，是基于opencv实现的。-This code is used to capture the video of a person s face and detect it by comparing the image detected with those already stored in the files.
camshift-code
- camshift目标跟踪代码：跟踪摄像头或者视频文件中的手动画出的目标区域-the code for camshift object detection and tracking
camshiftdemo
- 基于camshift算法的跟踪改进，适用于摄像头跟踪等--Tracking algorithm based on improved camshift for camera tracking, etc
OpenCV
- OpenCV CamShift. Real time hand tracking using backprojection , camshift and kalaman filter
CAMSHIFT
- 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。 该运行文件在VC6.0环境下编译通过，是一个 stand-alone 运行程序，不需要OPENCV的DLL库支持。在运行之前，请先连接好USB接口的摄像头。然后可以用鼠标选定欲跟踪目标。已经调通了。-CAMSHIFT algorithm using rapid detection and tracking moving targets C/C+
camshiftdemo
- Camshift Demo using OpenCV
Camshift
- Camshift 鼠标选择区域；进行人脸的跟踪-Camshift use mouse to select the area face tracking
camshift
- 实现视频图像的目标快速跟踪。使用了camshift和kalman滤波算法。实时性不错，代码简单。-Video images of fast target tracking. Use the camshift and kalman filtering algorithm. Timeliness good, simple code.
fall
- 混合高斯背景建模与CamShift算法结合的基于openCV的视频目标跟踪(OpenCV based video target tracking combined with hybrid Gauss background modeling and CamShift algorithm)