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2020 COCO Keypoint Challenge 冠军之路!

coco panoptic challenge 2020 leaderboard 作为唯一超过去年冠军的结果,Keypoint赛道里我们的成绩就很强吗? 另外一个有意思的事情是keypoint 榜上的结果是(1st,4th,7th)的成绩,意思是第二第三名并没有按照组委会要求提交报告,不知道成绩如何。 ? ---- 突破 在这里先介绍在keypoint这个问题上的两个改进,一个是关于codebase可靠性的,另外一个则是在监督方面让网络关注约束信息。 2020 COCO Keypoint Challenge Road Map 还记得HRNet横空出世时,我刚入coco keypoint的坑,那时觉得这指标太牛逼了,而且MSRA还是非常良心的,开源指标和论文一致 因为沿用top-down的方法(先检测人,然后对每个instance进行关键点定位),人体检测的效果对最后keypoint指标影响几乎是线性的,大概每提一个点的detection AP, keypoint

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干货 | COCO2018 Keypoint冠军算法解读

下面先上一个我们模型的视频结果:
COCO2018 Keypoint算法结果展示 Background 人体关键点检测(Human Keypoint Detection)又称为人体姿态识别 人体关键点检测任务对于现实生活有着很大的潜在用途,目前公开的比赛中最权威的是 MS COCO Keypoint track 的比赛,也是该领域最有挑战的比赛,参赛队不乏 Facebook,Google 旷视科技 Detection 组在 2017,2018 年两次夺得该比赛的冠军,2017 年旷视 COCO Keypoint 比赛冠军工作 CPN 在业界具有深远影响,并获得广泛使用。 这里,我们将介绍旷视 2018 年 COCO Keypoint 比赛夺冠的工作。 、人体动作识别,并在上述方向有着长期深入的研究;2017、2018 年作为负责人带队参加 COCO 人体姿态识别竞赛(Human Keypoint Detection),连续两次夺魁。

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    OpenPose: Keypoint Detection And Multi-Threading C++ Library

    Introduction OpenPose is a library for real-time multi-person keypoint detection and multi-threading Library main functionality: Multi-person 15 or 18-keypoint body pose estimation and rendering. Multi-person 2x21-keypoint hand estimation and rendering (coming soon in around 1-2 months!). Multi-person 70-keypoint face estimation and rendering (coming soon in around 2-3 months!). Tomas Simon and Hanbyul Joo and Iain Matthews and Yaser Sheikh}, booktitle = {CVPR}, title = {Hand Keypoint

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    Anchor-free目标检测综述 -- Keypoint-based篇

    anchor-free目标检测算法分为两种,一种是DenseBox为代表的Dense Prediction类型,密集地预测的框的相对位置,另一种则是以CornerNet为代表的Keypoint-bsaed 本文主要列举几种Keypoint-based Detection类型的网络,主要涉及以下网络: CornerNet ExtremeNet CenterNet CenterNet(Object as Point CenterNet [cf4ef5b0594cbd00a56b5cb52ba06477.png]   CornerNet将目前常用的anchor-based目标检测转换为keypoint-based目标检测 CornerNet-Lite   CornerNet作为Keypoint-based目标检测算法中的经典方法,虽然有着不错的准确率,但其推理很慢,大约需要1.1s/张。

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    揭秘 | 旷视研究院COCO 2018 Keypoint夺冠背后的技术

    第一期是我们 2018 年COCO Keypoint 冠军算法的首次解读。下面先上一个我们模型的视频结果。 视频内容 背 景 人体关键点检测(Human Keypoint Detection)又称为人体姿态识别,旨在准确定位图像之中人体关节点的位置,是人体动作识别、人体行为分析、人机交互的前置任务。 旷视研究院 Detection 组在 2017,2018 年两次夺得该比赛的冠军,2017 年旷视 COCO Keypoint 比赛冠军工作 CPN 在业界具有深远影响,并获得广泛使用。 这里,我们将介绍旷视 2018 年 COCO Keypoint 比赛夺冠的工作。 、人体动作识别,并在上述方向有着长期深入的研究;2017、2018 年作为负责人带队参加 COCO 人体姿态识别竞赛(Human Keypoint Detection),连续两次夺魁。

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    11种Anchor-free目标检测综述 -- Keypoint-based篇

    anchor-free目标检测算法分为两种,一种是DenseBox为代表的Dense Prediction类型,密集地预测的框的相对位置,另一种则是以CornerNet为代表的Keypoint-bsaed 本文主要列举几种Keypoint-based Detection类型的网络,主要涉及以下网络: CornerNet ExtremeNet CenterNet CenterNet(Object CornerNet将目前常用的anchor-based目标检测转换为keypoint-based目标检测,使用角点对表示每个目标,CornerNet主要关注目标的边界信息,缺乏对目标内部信息的获取,很容易造成误检 论文地址:https://arxiv.org/abs/1904.08900 论文代码:https://github.com/princeton-vl/CornerNet-Lite CornerNet作为Keypoint-based

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    CornerNet:经典keypoint-based方法,通过定位角点进行目标检测 | ECCV2018

    论文: CornerNet: Detecting Objects as Paired Keypoints

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    SIFT特征检测(一)

    descriptor. len = 6 * keypoint(3); % Rotate the keypoints by 'ori' = keypoint(4) s = sin(keypoint(4 )); c = cos(keypoint(4)); % Apply transform r1 = keypoint(1) - len * (c * y1 + s * x1); c1 = keypoint (2) + len * (- s * y1 + c * x1); r2 = keypoint(1) - len * (c * y2 + s * x2); c2 = keypoint(2) + len * keys1, Image im2, Keypoint keys2); Keypoint CheckForMatch(Keypoint key, Keypoint klist); int DistSquared Otherwise, return NULL. */ Keypoint CheckForMatch(Keypoint key, Keypoint klist) { int dsq, distsq1

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    PCL点云特征描述与提取(4)

    ; //narf_keypoint_detector为点云对象 narf_keypoint_detector.setRangeImageBorderExtractor ().support_size = support_size; //获得特征提取的大小 pcl::PointCloud<int> keypoint_indices; narf_keypoint_detector.compute (keypoint_indices); std::cout << "Found "<<keypoint_indices.points.size ()<<" key points. ; keypoint_indices2.resize (keypoint_indices.points.size ()); for (unsigned int i=0; i<keypoint_indices.size (); ++i) // This step is necessary to get the right vector type keypoint_indices2[i]=keypoint_indices.points

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    半监督关键点本地化

    However, supervised training of a keypoint detection network requires annotating a large image dataset keypoint representations in a semi-supervised manner using a small set of labeled images along with Keypoint representations are learnt with a semantic keypoint consistency constraint that forces the keypoint detection network to learn similar features for the same keypoint across the dataset. Pose invariance is achieved by making keypoint representations for the image and its augmented copies

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    图像识别基本算法之SURF

    SurfFeatureDetector surfDetector(4000); //hessianThreshold,海塞矩阵阈值,并不是限定特征点的个数 vector<KeyPoint > keyPoint1,keyPoint2; surfDetector.detect(image1,keyPoint1); surfDetector.detect(image2 ,keyPoint2); //绘制特征点 drawKeypoints(image1,keyPoint1,image1,Scalar::all(-1),DrawMatchesFlags ,imageDesc1); SurfDescriptor.compute(image2,keyPoint2,imageDesc2); //特征点匹配并显示匹配结果 ,image02,keyPoint2,goodMatchePoints,imageOutput,Scalar::all(-1), Scalar::all(-1),vector<char

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    MPII姿态估计性能评价标准-PCK

    code from mmpose def keypoint_pck_accuracy(pred, gt, mask, thr, normalize): """Calculate the pose gt (np.ndarray[N, K, 2]): Groundtruth keypoint location. Returns: tuple: A tuple containing keypoint accuracy. - acc (np.ndarray[K]): Accuracy of each keypoint. targets (np.ndarray[N, K, D]): Groundtruth keypoint location.

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    SIFT matlab源代码解析

    for i = 1: size(locs,1) % Draw an arrow, each line transformed according to keypoint parameters. parameters. % % Parameters: % Arrays: % imsize = [rows columns] of image % keypoint = [subpixel_row descriptor. % 长度放大6倍 len = 6* keypoint(3); % Rotate the keypoints by 'ori' = keypoint(4) s = sin(keypoint (4)); c = cos(keypoint(4)); % Apply transform %画一条线需要起点和终点,两个点所以搞出四个坐标 r1 = keypoint(1) - len * (c * y1 + s * x1); c1 = keypoint(2) + len * (- s * y1 + c * x1); r2 = keypoint(1) - len * (c * y2 + s *

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    Caffe2 - (三十) Detectron 之 modeling - 模型_heads

    网络设计: ... -> RoI ----\ -> RoIFeatureXform -> keypoint head -> keypoint output -> loss ... -> Feature / Map keypoint head 产生 RoI 的特征表示,用于预测 keypoint. keypoint 输出模块将特征表示转化为 keypoint (model, blob_in, dim): """ 添加 Mask R-CNN keypoint 输出: keypoint heatmaps. """ # NxKxHxW (假设极端情况时,只有一个可见keypoint 和 N 个可见 keypoints: N 个 keypoints时,每个对于梯度计算的作用是 1/N; 而 1 个 keypoint (意味着,一个可见 keypoint 与 N 个 keypoints 中的每个 keypoint 的作用效果相同.) """ model.StopGradient('keypoint_loss_normalizer

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    浅谈opencv自动光学检测、目标分割和检测(连通区域和findContours)

    算子特征点检测 int minHessian = 700; cv::SurfFeatureDetector detector(minHessian);//定义特征点类对象 std::vector<cv::KeyPoint keyPoint1, keyPoint2;//存放动态数组,也就是特征点 detector.detect(srcImg1, keyPoint1); detector.detect(srcImg2 , keyPoint2); //特征向量 cv::SurfDescriptorExtractor extrator;//定义描述类对象 cv::Mat descriptor1, descriptor2 ;//描述对象 extrator.compute(srcImg1, keyPoint1, descriptor1); extrator.compute(srcImg2, keyPoint2, descriptor2 , srcImg2, keyPoint2, matches, imgMatch); cv::namedWindow("匹配图", CV_WINDOW_AUTOSIZE); cv::imshow("匹配图

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    高翔Slambook第七讲代码解读(三角测量)

    我们先来看一下子函数声明: void find_feature_matches ( const Mat& img_1, const Mat& img_2, std::vector<KeyPoint _2d2d ( const std::vector<KeyPoint>& keypoints_1, const std::vector<KeyPoint>& keypoints_2, const std::vector< DMatch >& matches, Mat& R, Mat& t ); void triangulation ( const vector<KeyPoint >& keypoint_1, const vector<KeyPoint>& keypoint_2, const std::vector< DMatch >& matches, void triangulation ( const vector< KeyPoint >& keypoint_1, const vector< KeyPoint >& keypoint

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    实录 | 旷视研究院详解COCO2017人体姿态估计冠军论文(PPT+视频)

    从人理解关节点的过程,我们受到了很大的启发,但是怎么样做才可以把看keypoint这个过程体现在卷积神经网络里呢。 Hard NMS的不同阈值也会有所影响,阈值越高,keypoint精度越高。 这里与detection的检测目标与keypoint检测的目标不同有关系。 ? 介绍下我们网络。 ? Q&A 在视频中进行Keypoint检测怎么加入tracking?能否用keypoint检测做行人检测和行人多目标跟踪? 能否用keypoint检测做行人检测和行人多目标跟踪这个的话,其实在做行人检测上,可以用keypoint的检测结果做post filter滤掉行人检测的FP。

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    ubuntu16.0.4 opencv4.0.0 GPU 版本的 SURF

    opencv4.0.0 自带的 samples GPU surf_keypoint_matcher.cpp surf_keypoint_matcher.cpp #include <iostream features detector, descriptor extractor and BruteForceMatcher_CUDA" << endl; cout << "\nUsage:\n\tsurf_keypoint_matcher matcher->match(descriptors1GPU, descriptors2GPU, matches); // downloading results vector<KeyPoint zhangjun/SoftWare/opencv-4.0.0/build") find_package( OpenCV REQUIRED ) add_executable( SURF_test surf_keypoint_matcher.cpp

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    Github 项目 - OpenPose 参数说明

    --write_keypoint path/: 在指定路径输出包含人体姿态数据的 JSON, XML 或 YML 文件. (hand_scale_number, 1, “Analogous to scale_number but applied to the hand keypoint detector. , “”, “(Deprecated, use write_json) Directory to write the people pose keypoint data. Set format with write_keypoint_format.”); **[已废弃] ** DEFINE_string(write_keypoint_format, “yml”, “(Deprecated , use write_json) File extension and format for write_keypoint: json, xml, yaml & yml.

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    高翔Slambook第七讲代码解读(2d-2d位姿估计)

    namespace cv; void find_feature_matches ( const Mat& img_1, const Mat& img_2, std::vector<KeyPoint _2d2d ( const std::vector<KeyPoint> & keypoints_1, const std::vector<KeyPoint> & keypoints_2, > & keypoints_1, const std::vector<KeyPoint> & keypoints_2, const Mat& R, const Mat& t, const std vector< KeyPoint >& keypoints_2, const vector< DMatch >& matches, Mat& R, Mat& t ) { // 相机内参,TUM verify_polar_constraint void verify_polar_constraint( const vector< KeyPoint >& keypoints_1, const

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