搜索资源列表
tract
- OpenCv实现视频中运动物体跟踪 三帧差法 滤波 绘制矩形框区-OpenCv tracking moving objects in video three difference method draws a rectangle filtering area
detection-and-tracking
- 视频演示算法包括: 1. 静态背景下的背景预测法目标检测 2. 静态背景下帧间差分法目标检测 3. Mean Shift目标跟踪方法 4. 重心多目标跟踪方法-Algorithm for video presentation include: 1. Static background background prediction target detection 2. Static background frame difference method for
TrackandID1
- 基于opencv环境在视频区域内设置感兴趣区域,在该区域内进行车辆检测以及车辆跟踪。-Based on the opencv environment settings in the video area of the region of interest in the region for vehicle detection, and vehicle tracking.
MeanShift-Kalman-tracking-algorithm
- 结合kalman滤波的meanshift跟踪算法,meanshift算法是一种经典的均值漂移跟踪算法,采用颜色直方图作为跟踪特征,具有很高的稳定性和鲁棒性,kalman滤波是一种线性滤波预测算法。在meanshift算法中引入kalman滤波,可以提高算法的稳定性,同时提高算法执行效率。代码中除了avi格式视频读取采用opencv库函数外,核心算法均为纯C++实现,注释详细,很容易理解,同时具有很高的参考价值。 -Meanshift kalman filtering combined track
Opencv_vedio-process
- 使用Opencv编写的视频处理程序,能够实现跟踪,增强和彩色化!-Opencv prepared using video processing procedures to achieve tracking, enhancement and colorization!
kalman_camshift-object-tracking
- 基于vs2010+opencv2.4.4调试通过的kalman_camshift跟踪运动物体,测试视频比较简单。-Based on vs2010+ opencv2.4.4 through debugging kalman_camshift tracking moving objects, test video is relatively simple.
motiondetect
- opencv环境下实现对视频中运动目标的识别与跟踪,内含视频为某时段某市区交通情况-opencv environment to achieve the video moving target identification and tracking, embedded video for a certain period of urban traffic conditions
video-processing-
- 视频演示算法包括: 1. 静态背景下的背景预测法目标检测 2. 静态背景下帧间差分法目标检测 3. Mean Shift目标跟踪方法 4. 重心多目标跟踪方法 -Algorithm for video presentation include: 1. Static background background prediction target detection 2. Static background frame difference method for target d
particlefiltervs2010
- 粒子滤波程序,可用于目标跟踪,可以处理视频文件或是摄像头采集,仅供学习参考-Particle filter can be used for target tracking, can handle video files or camera capture reference for learning
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- 了适应跟踪过程中目标光照条件的变化,并对目标特征进行在线更新,提出一种将局部二元模式(LBP) 特征与图像灰度信息相融合,同时结合增量线性判别分析对目标进行跟踪的算法.跟踪开始前,为了获得比较准确的目标描述,使用混合高斯模型和期望最大化算法对目标进行分割;跟踪过程中,通过蒙特卡罗方法对目标区域和背景区域进行采样,并更新特征空间参数.得到目标和背景的最优分类面;最后使用粒子滤波器结合最优分类面对目标状态进行预测.通过光照变化的仿真视频和自然场景视频的跟踪实验,验证了文中算法的有效性.-Trac
matlab-Tracking
- 动态目标的识别与跟踪程序程序,该程序在MATLAB平台可运行,有测试视频,对于学习目标跟踪有较大帮助-Dynamic target recognition and tracking program program that can be run in the MATLAB platform, a test video for the track great help learning objectives
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- 光流算法程序,用来估计背景变化的图像序列或视频序列,可以很好的用来跟踪运动目标-Optical flow algorithm is used to estimate changes in background image sequence or video sequence can be well used to track moving targets. . .
RGB_Detection_and_Tracking
- 检测和跟踪视频中的红色物体.并且能够使用检测到的物体的位置在控制光标在屏幕上的位置。-detect and track red, green and blue objects in Live Video
Demo-Motion-Detection
- Matlab编写的一段程序,利用文档中的Demo文件,通过函数的调用,实现对视频文件中运动目标的跟踪-Matlab prepared for some procedures, the use of the document Demo files through function calls to achieve the video moving target tracking file
Video-Demo
- 视频演示算法包括: 1. 静态背景下的背景预测法目标检测 2. 静态背景下帧间差分法目标检测 3. Mean Shift目标跟踪方法 4. 重心多目标跟踪方法-Algorithm for video presentation include: 1 Static background background prediction target detection 2 Static background frame difference method for target de
video-motion-detection
- 一段视频中运动目标检测跟踪以及画出轨迹设计-Video motion detection
kalman-matlab
- 利用matlab实现的视频目标跟踪算法,利用kalman滤波方法,实时有效的实现跟踪,准确性比较高。-Using matlab realize the video target tracking algorithm using kalman filtering method to achieve real and effective tracking, accuracy is relatively high.
MeanShift-matlab
- MeanShift运动目标跟踪 matlab程序 思路: 1.截取跟踪目标矩阵rect 2.求取跟踪目标的加权直方图hist1 3.读取视频序列中的一帧, 先随机取一块与rect等大的矩形,计算加权直方图hist2。 4.计算两者比重函数,如果后者差距过大, 更新新的矩阵中心Y,进行迭代(MeanShift是一种变步长可以迅速接近概率密度峰值的方法),直至一定条件后停止。 -MeanShift moving target tracking matlab program
Target-trace
- 利用Opencv对运动目标(包括汽车,行人)的AVI视频进行检测并跟踪。效果十分显著。 - detect and tracking the moving targets (including cars, pedestrians) AVI video detection and tracking. Effect is very significant.
sms_optic_flow_2.0.tar
- High Accuracy Optical Flow Estimation Based on a Theory for Warping. 一种健壮的稠密光流算法,可以用于目标对象跟踪、视频稳像-High Accuracy Optical Flow Estimation Based on a Theory for Warping. An robust optical flow estimation algorithm