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VideosTargetDetection.rar
- 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 ,1. Static background prediction in the context of target detection method 2. Static background frame difference method for target detection 3. Mean Shift Object Tracking Method 4.
Video_detection
- 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 该框架支持的视频只限于RGB非压缩Windows AVI格式，可以通过“文件”菜单下打开视频来打开视频文件。-Algorithm for video presentation include: 1. Static background background prediction target detection 2.
videoprocessframework
- 目标检测演示框架算法包括： 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 该框架支持的视频只限于RGB非压缩Windows AVI格式，可以通过“文件”菜单下打开视频来打开视频文件。-Presentation framework of the target detection algorithms include: 1. The context of the background s
Video_Demo
- 视频目标检测演示框架,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 -Framework for object detection video presentation, video presentation algorithms include: 1. Static background of the context of target detection pr
tracing-to-moving-object
- 采用了一种根据灰度特征进行模式匹配的跟踪算法提高了模板匹配法对于目标运动姿 态变化的自适应能力仿真结果表明上述算法在目标方向平移旋转以及图像背景对比度亮度发生改变时均能较好的检测到目标-It is presented in this paper that a modified version of it which incorporating with frame difference and multi-resolution matching. By introducing pre
hed-master
- We develop a new edge detection algorithm, holistically-nested edge detection (HED), which performs image-to-image prediction by means of a deep learning model that leverages fully convolutional neural networks and deeply-supervised nets. HED automat