源代码2
图像视觉领域部分开源代码2
场景识别:
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust
Semantic Pixel-Wise Labelling
https://github.com/alexgkendall/caffe-segnet
Tracking:
Learning to Track: Online Multi-Object Tracking by Decision Making ICCV2015
使用 Markov Decision Processes 做跟踪,速度可能比较慢,效果应该还可以
https://github.com/yuxng/MDP_Tracking
Car detection:
Integrating context and Occlusion for Car Detection by Hierarchical And-or Model ECCV2014
http://www.stat.ucla.edu/~boli/projects/context_occlusion/context_occlusion.html
Face detection
人脸检测2015进展:http://www.cvrobot.net/latest-progress-in-face-detection-2015/
Face detection without bells and whistles
project:http://markusmathias.bitbucket.org/2014_eccv_face_detection/
Code:https://bitbucket.org/rodrigob/doppia
Talk: http://videolectures.net/eccv2014_mathias_face_detection/ (不错的报告)
From Facial Parts responses to Face Detection: A Deep Learning APProach ICCV2015 email to get code and model
http://personal.ie.cuhk.edu.hk/~ys014/projects/Faceness/Faceness.html
A Fast and Accurate Unconstrained Face Detector 2015 PAMI
简单 快速 有效
http://www.cbsr.ia.ac.cn/users/scliao/projects/npdface/
Face Alignment
Face Alignment by Coarse-to-Fine Shape Searching
http://mmlab.ie.cuhk.edu.hk/projects/CFSS.html
Face recognition
Deep face recognition
http://www.robots.ox.ac.uk/~vgg/software/vgg_face/
Person Re-identification :
Query-adaptive Late Fusion for Image Search and Person Re-identification
CVPR2015
http://www.liangzheng.com.cn/Project/project_fusion.html
Efficient Person Re-identification by Hybrid Spatiogram and Covariance Descriptor CVPR2015 Workshops
https://github.com/Myles-ZMY/HSCD
Person Re-Identification by Iterative Re-Weighted Sparse Ranking PAMI 2015
http://www.micc.unifi.it/masi/code/isr-re-id/ 没有特征提取代码
Person re-identification by local Maximal Occurrence representation and metric learning CVPR2015
http://www.cbsr.ia.ac.cn/users/scliao/projects/lomo_xqda/
Head detection
Context-aware CNNs for person head detection
Matlab code & dataset avaiable
http://www.di.ens.fr/willow/research/headdetection/
Pedestrian detection
Pedestrian Detection with Spatially pooled Features and structured Ensemble Learning PAMI 2015
Strengthening the Effectiveness of Pedestrian Detection with Spatially Pooled Features ECCV2014
https://github.com/chhshen/pedestrian-detection
Object detection
Object detection via a multi-region & semantic segmentation-aware CNN model
https://github.com/gidariss/mrcnn-object-detection
DeepBox: Learning Objectness with Convolutional Networks ICCV2015
proposal re-ranker
https://github.com/weichengkuo/DeepBox
Object-Proposal Evaluation Protocol is ‘Gameable’ 好多 Proposal 代码
https://github.com/BATra-mlp-lab/object-proposals
Deep Learning
Deeply Learned Attributes for Crowded Scene understanding
https://github.com/amandajshao/www_deep_crowd
http://www.ee.cuhk.edu.hk/~jshao/WWWCrowdDataset.html
Human Pose Estimation
DeepPose: Human Pose Estimation via Deep Neural Networks, CVPR2014
https://github.com/mitmul/deeppose not official implementation
Articulated Pose Estimation by a Graphical Model with Image Dependent pairwise Relations NIPS 2014
http://www.stat.ucla.edu/~xianjie.chen/projects/pose_estimation/pose_estimation.html
Learning Human Pose Estimation Features with Convolutional Networks
https://github.com/stencilman/deep_nets_iclr04
Flowing ConvNets for Human Pose Estimation in Videos
http://www.robots.ox.ac.uk/~vgg/software/cnn_heatmap/
杂项
Unsupervised Learning of Visual Representations using Videos 很有前途啊!
https://github.com/xiaolonw/caffe-video_triplet
数据库
MPII Human Pose Dataset
http://human-pose.mpi-inf.mpg.de/#overview
DPM
将voc-release4.0.1 linux 转到windows
http://blog.csdn.net/masibuaa/article/details/17577195
开源车牌识别代码
支持美国和欧洲车牌
http://www.openalpr.com
参考文献:
-
http://www.cnblogs.com/einyboy/p/3594432.html
-
http://blog.csdn.net/cv_family_z/article/details/49902011
相关阅读
此文档关于BANNER的规范设置,只是规范在目前情况下,为了提高团队伙伴们的工作效率和维护平台风格的统一而制定的,但是,作为一名合格的
相机成像模型的雏形是小孔成像(pinhole):双目视觉的视差原理基于此:以此可以计算形成图像的大小。下面求视差:假设两个相机的内部参
阿拓带你飞:视觉设计师也会迷茫,遇到瓶颈了该怎么办?一方面不想发展成管理者,想做高等级的设计师。另一方面对未来总还是有点不知道如
笔者从需求方的角度出发,从三个维度——appearance颜值、experience体验、function功能,来为大家解读:清晰视觉设计的本质是什么?视觉
提高视觉冲击力的方法非常非常多,我将结合下我的平面设计经验,总结归纳一下我所学习到的平面设计理论。我一直觉得学习平面设计不能