资源列表
design
- 用opencv实现camshift算法的手势跟踪，并且使用了 kalman算法来预测物体可能的运动轨迹-Camshift with opencv gesture tracking algorithm, and the use of kalman algorithm to predict the possible trajectories of objects
yanse_shibie
- 摄像头摄取图像后，用两种常见的连通区域分析和标记的算法：1）Two- pass；2）Seed-Filling种子填充，并给出了两个算法的基于OpenCV的C++实现代码。-After the camera takes an image, analyze, and marked with two common regional connectivity algorithm: 1) Two-pass 2) Seed-Filling seed filling, and gives two algor
beijingPzhenjian
- 实现surendra背景差分，并和帧间差分比较效果-Achieve surendra background difference, and the comparative effectiveness of inter-frame difference
lizilvbogenzong
- 利用粒子滤波实现运动目标跟踪的c++源码，效果比较好-Particle filter tracking moving objects c++ source, the effect is better
duomubiaogenzong
- 五篇利用opencv不同方法的多运动目标检测跟踪的论文-Five different methods using opencv target detection and tracking of multiple moving papers
bianyuan
- 图像的边缘提取,使用opencv2.4.4与vs2010,检测数图像中物体边缘-HOG extract
t1
- opencv学习,camshift,能实现物体跟踪，入门学习的简单例程-opencv learning, camshift, object tracking can be achieved, started to learn the simple routines
opencv
- 图像场景分类的bow模型opencv源代码，采用k-means聚类构造单词，采用支持向量机的svm分类器。-Image scene classification bow model opencv source code, using k-means clustering structure of words, using support vector machine svm classifier.
opencv_detect
- opencv中常用的检测和跟踪算法原理介绍,介绍了常用的前景检测与目标跟踪算法-opencv principle commonly used in the detection and tracking algorithm, the introduction of the common prospects for detection and target tracking algorithm
testOpencv
- 实现了运动目标的检测与跟踪，根据两帧图像的帧差来判断该目标是否在运动-Achieve a moving target detection and tracking, based on frame difference two frames to determine if the target is in motion
12354
- 实现了多高斯建模法的视频分割算法和越界检测、运动物体尺寸检测、计数等应用。算法主要由OPENCV实现。 软件目前可实现以下功能： 1）提供高斯建模法研究相应算法实现的效果的影响； 2）可以实现原视频与处理后的视频同时播放，实现跟踪； 3）实现车流量技术计数。 -To achieve a multi-Gaussian modeling method of video segmentation algorithm and cross-border detection, movin
OpenCv--based-image-retrieval-DEMO
- 基于OpenCv开发的图像检索DEMO，速度较快，效果较好，可以作为一般意义下的图像相似度检索服务！根据具体项目改改即可使用！-OpenCv developed based image retrieval DEMO, faster, better, can be used as image similarity retrieval services in a generic sense! Changed to use the specific project!