搜索资源列表
HOG-LBP-detection
- 该程序分别提取正负样本图像的HOG和LBP特征，利用支持向量机进行样本训练，得到行人分类器。利用训练好的分类器进行检测，实验结果表明，该方法可以有效检测出图像中的行人，并达到了较好的检测结果。-A novel approach based on combining Histogram of oriented gradients (HOG) and LocalBinary Pattern(LBP) is suggested in the program.Also liner SVM is acte
Pedestrian-detection-and-tracking
- 行人检测与跟踪，是基于opencv和vc6.0-Pedestrian detection and tracking is based on opencv and vc6.0
PedestrianDetect
- 基于hog的行人检测,基于hog的行人检测 -people detection base on hog
The-pedestrian-contour-detection
- 可以完整的进行行人轮廓的检测及背景提取和分析-Can complete pedestrian contour detection and background extraction and analysis
People_Detect_2008
- opencv实现行人检测的代码程序,适合初学者！可以适当更改！-Opencv pedestrian detection technology
smartcar
- 基于单片机的智能小车，可实现障碍物及行人的自动检测，并判断烟雾是否达标-Microcontroller-based smart car, can automatically detect obstacles and pedestrians, and determines whether the target smoke
pedestrians128x64
- matlab行人检测所需要的行人图片素材，128*64像素图片-matlab pedestrian detection require pedestrians picture material, 128* 64 pixel image
python-行人检测-可用
- 这是利用python+opencv编写的，行人检测代码，另配有检测样图，测试可用(this is a code for person detect,it function is normal)
pedestrian
- 能够实时检测行人，对其进行画框，标注，坐标存储，实现对后期行人属性分析做准备~(Can detect the pedestrian in real time, make the frame, mark, coordinate storage, realize the analysis of the late pedestrian property)
py-faster-rcnn-master
- fast-rcnn源码,可用于快速目标检测，如行人识别 车辆识别 车标识别(This is an sourcecode for python fast-rcnn)
hog
- 基于hog算法的6篇论文，主要简述对行人的检测(6 papers based on the hog algorithm mainly describe the detection of pedestrians)
xingrengenzong
- 改程序设计是基于差分法的行人检测，能框出人行走的轨迹。(The design of the modified program is based on the difference method of pedestrian detection, which can frame the track of human walking.)
基于卡尔曼滤波的运动目标检测，matlab代码
- 基于卡尔曼滤波算法的运动目标检测，做视频车道线检测，动态车辆识别，行人检测的朋友可以参考一下(Based on Calman filter algorithm for moving object detection, do video lane detection, dynamic vehicle recognition, pedestrian detection friend can refer to.)
行人检测统计
- 该行人统计方法柔性较好，针对不同的应用场景参数调整较少。实时性好，在普通PC上能够实现30FPS的检测速度。统计准确率较高，当行人密度低时能达到90%以上的准确率，当行人密度高时能达到70%以上的准确率。初步达到了实际应用需求。(The pedestrian statistical method is flexible and less adjustable for different application scenarios. It has good real-time performanc