搜索资源列表
bodymotiondetection
- 学习opencv图像处理中人体目标跟踪的一些很有用的资料，主要是讲camshift,meanshift和高斯混合模型。-Learning opencv image-processing for target tracking in the human body a number of very useful information, mainly speaking camshift, meanshift and Gaussian mixture model.
camshiftdemo
- 利用目标的颜色直方图模型转化为颜色概率分布图，进行跟踪。-the Color histogram model of goal convert color of probability distribution ,then tracking
fulltext
- 一个实时的2维人体检测跟踪算法，是opencv源码里参考的文章之一。内容包括表面模型建立，新目标检测以及匹配的问题。-A Real-Time System for Detecting and Tracking People in 2D, is one of the references in opencv, including the appearance model, new object detect and how to find the corresponding one.
GaussDetect
- 基于高斯混合运动背景模型的运动目标检测和跟踪源程序-Moving target detection and tracking the source of the background model based on Gaussian mixture motion
KaewTraKulPong
- opencv中混合高斯模型实现方法的参考文献-An Improved Adaptive Background Mixture Model for Real-time Tracking and Shadow Detection
TrackEye_SourceCode
- 跟踪人的眼睛，类似人脸识别算法中的adaboost，通过学习人眼模式，实现人眼检测跟踪。-Tracking people' s eyes, similar face recognition algorithm adaboost by learning human eye model, detecting and tracking the human eye.
bpass
- the program based on tracking for a new algorithm, Integrated Bayesian MCMC Model Selection MONTE CARLO that Ma Erkefu chain
camshift
- 基于camshift的目标跟踪源代码，了能更好地解决在动态模型为非线性且噪声为非高斯的条件下对机动目标的。- 英语 中文(简体) 日语 Based on the target tracking camshift source code, has been able to better address the dynamic model of nonlinear non-Gaussian and t
TLD
- 本程序是在vs2010+opencv的平台上运行的，利用的是opencv2.4.9，C++写的，该算法与传统跟踪算法的显著区别在于将传统的跟踪算法和传统的检测算法相结合来解决被跟踪目标在被跟踪过程中发生的形变、部分遮挡等问题。同时，通过一种改进的在线学习机制不断更新跟踪模块的“显著特征点”和检测模块的目标模型及相关参数，从而使得跟踪效果更加稳定、鲁棒、可靠。最后的得到的效果令人满意。-This program is run on a platform vs2010+opencv, the use
Face_Recognition
- 基于opencv库中的人脸识别代码，可以实现检测并跟踪视频中的人脸，可以选择训练人脸模型，或者直接加载训练好的人脸模型来识别视频中的人脸身份。-Based opencv library, this face recognition code can be achieve detecting and tracking faces in the video. You can choose training face model, or load directly trained human face
Tracking-of-moving-objects
- 是经典的blob模型对实现对运动物体的跟踪，很好的例子，适合于初学者。-It is the classic model for achieving blob tracking moving objects, a good example, suitable for beginners.