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class="octo-body"/></svg></a><main id="main" class="main"><div class="main-inner"><div class="content-wrap"><div id="content" class="content"><section id="posts" class="posts-expand"><article class="post post-type-normal" itemscope itemtype="http://schema.org/Article"><div class="post-block"><link itemprop="mainEntityOfPage" href="https://huangpiao.tech/2020/07/04/Waymo 2020 2d和3d目标检测与跟踪冠军解决方案/"><span hidden itemprop="author" itemscope itemtype="http://schema.org/Person"><meta itemprop="name" content="黄飘"><meta itemprop="description" content="直到这一刻微笑着说话为止，我至少留下了一公升眼泪"><meta itemprop="image" content="/images/author.jpg"></span><span hidden itemprop="publisher" itemscope itemtype="http://schema.org/Organization"><meta itemprop="name" content="见渊の博客"></span><header class="post-header"><h2 class="post-title" itemprop="name headline"><a href="/2020/07/04/Waymo 2020 2d和3d目标检测与跟踪冠军解决方案/" class="post-title-link" itemprop="url">Waymo2020 | 2D/3D目标检测、跟踪和域自适应性冠军解决方案解析</a></h2><div class="post-meta"><span class="post-time"><span class="post-meta-item-icon"><i class="fa fa-calendar-o"></i> </span><span class="post-meta-item-text">发表于</span> <time title="创建时间：2020-07-04 23:00:00" itemprop="dateCreated datePublished" datetime="2020-07-04T23:00:00+08:00">2020-07-04</time> </span><span class="post-category"><span class="post-meta-divider">|</span> <span class="post-meta-item-icon"><i class="fa fa-folder-o"></i> </span><span class="post-meta-item-text">分类于</span> <span itemprop="about" itemscope itemtype="http://schema.org/Thing"><a href="/categories/机器学习/" itemprop="url" rel="index"><span itemprop="name">机器学习</span></a></span> ， <span itemprop="about" itemscope itemtype="http://schema.org/Thing"><a href="/categories/机器学习/目标跟踪/" itemprop="url" rel="index"><span itemprop="name">目标跟踪</span></a></span> </span><span class="post-comments-count"><span class="post-meta-divider">|</span> <span class="post-meta-item-icon"><i class="fa fa-comment-o"></i> </span><a href="/2020/07/04/Waymo 2020 2d和3d目标检测与跟踪冠军解决方案/#comments" itemprop="discussionUrl"><span class="post-meta-item-text">评论数：</span> <span class="post-comments-count valine-comment-count" data-xid="/2020/07/04/Waymo 2020 2d和3d目标检测与跟踪冠军解决方案/" itemprop="commentCount"></span></a></span><div class="post-symbolscount"><span class="post-meta-item-icon"><i class="fa fa-file-word-o"></i> </span><span class="post-meta-item-text">本文字数：</span><span title="本文字数">2.8k字</span></div></div></header><div class="post-body" itemprop="articleBody"><hr><h2 id="前言"><a href="#前言" class="headerlink" title="前言"></a>前言</h2><blockquote><p>随着最近Waymo Open Dataset Challenges 2020 的落幕，其中关于2D/3D目标检测和跟踪赛道的部分冠亚军解决方案也都公布了，由于我只看到了地平线发布的论文，所以就只分析他们公司在这次比赛中的解决方案。</p><p><strong>PS:地平线在Waymo2020中获得了3D检测、2D/3D跟踪和域自适应性赛道冠军，2D检测赛道亚军。</strong></p></blockquote><div class="post-button text-center"><a class="btn" href="/2020/07/04/Waymo 2020 2d和3d目标检测与跟踪冠军解决方案/#more" rel="contents">阅读全文 »</a></div></div><div></div><footer class="post-footer"><div class="post-eof"></div></footer></div></article><article class="post post-type-normal" itemscope itemtype="http://schema.org/Article"><div class="post-block"><link itemprop="mainEntityOfPage" href="https://huangpiao.tech/2020/06/27/基于深度学习的多目标跟踪算法（下）——ReID与MOT的联系/"><span hidden itemprop="author" itemscope itemtype="http://schema.org/Person"><meta itemprop="name" content="黄飘"><meta itemprop="description" content="直到这一刻微笑着说话为止，我至少留下了一公升眼泪"><meta itemprop="image" content="/images/author.jpg"></span><span hidden itemprop="publisher" itemscope itemtype="http://schema.org/Organization"><meta itemprop="name" content="见渊の博客"></span><header class="post-header"><h2 class="post-title" itemprop="name headline"><a href="/2020/06/27/基于深度学习的多目标跟踪算法（下）——ReID与MOT的联系/" class="post-title-link" itemprop="url">基于深度学习的多目标跟踪算法（下）——ReID与MOT的联系</a></h2><div class="post-meta"><span class="post-time"><span class="post-meta-item-icon"><i class="fa fa-calendar-o"></i> </span><span class="post-meta-item-text">发表于</span> <time title="创建时间：2020-06-27 19:55:00" itemprop="dateCreated datePublished" datetime="2020-06-27T19:55:00+08:00">2020-06-27</time> <span class="post-meta-divider">|</span> <span class="post-meta-item-icon"><i class="fa fa-calendar-check-o"></i> </span><span class="post-meta-item-text">更新于</span> <time title="修改时间：2020-07-09 19:55:00" itemprop="dateModified" datetime="2020-07-09T19:55:00+08:00">2020-07-09</time> </span><span class="post-category"><span class="post-meta-divider">|</span> <span class="post-meta-item-icon"><i class="fa fa-folder-o"></i> </span><span class="post-meta-item-text">分类于</span> <span itemprop="about" itemscope itemtype="http://schema.org/Thing"><a href="/categories/机器学习/" itemprop="url" rel="index"><span itemprop="name">机器学习</span></a></span> ， <span itemprop="about" itemscope itemtype="http://schema.org/Thing"><a href="/categories/机器学习/目标跟踪/" itemprop="url" rel="index"><span itemprop="name">目标跟踪</span></a></span> </span><span class="post-comments-count"><span class="post-meta-divider">|</span> <span class="post-meta-item-icon"><i class="fa fa-comment-o"></i> </span><a href="/2020/06/27/基于深度学习的多目标跟踪算法（下）——ReID与MOT的联系/#comments" itemprop="discussionUrl"><span class="post-meta-item-text">评论数：</span> <span class="post-comments-count valine-comment-count" data-xid="/2020/06/27/基于深度学习的多目标跟踪算法（下）——ReID与MOT的联系/" itemprop="commentCount"></span></a></span><div class="post-symbolscount"><span class="post-meta-item-icon"><i class="fa fa-file-word-o"></i> </span><span class="post-meta-item-text">本文字数：</span><span title="本文字数">3.1k字</span></div></div></header><div class="post-body" itemprop="articleBody"><h2 id="前言"><a href="#前言" class="headerlink" title="前言"></a>前言</h2><blockquote><p>最近基于深度学习的多目标跟踪算法越来越多，有用于特征提取的，有改进单目标跟踪器的，也有提升数据关联的。如果真的要总结的话那就太多了，所以我准备分类别进行介绍，这次是这一系列最后一篇。我主要介绍基于行人重识别(ReID)算法的方法，相关MOT的基础知识可以去我的<a href="https://zhuanlan.zhihu.com/ise-mot">专栏</a>查看。</p></blockquote><div class="post-button text-center"><a class="btn" href="/2020/06/27/基于深度学习的多目标跟踪算法（下）——ReID与MOT的联系/#more" rel="contents">阅读全文 »</a></div></div><div></div><footer class="post-footer"><div class="post-eof"></div></footer></div></article><article class="post post-type-normal" itemscope itemtype="http://schema.org/Article"><div class="post-block"><link itemprop="mainEntityOfPage" href="https://huangpiao.tech/2020/06/26/基于GNN的多目标跟踪算法解析/"><span hidden itemprop="author" itemscope itemtype="http://schema.org/Person"><meta itemprop="name" content="黄飘"><meta itemprop="description" content="直到这一刻微笑着说话为止，我至少留下了一公升眼泪"><meta itemprop="image" content="/images/author.jpg"></span><span hidden itemprop="publisher" itemscope itemtype="http://schema.org/Organization"><meta itemprop="name" content="见渊の博客"></span><header class="post-header"><h2 class="post-title" itemprop="name headline"><a href="/2020/06/26/基于GNN的多目标跟踪算法解析/" class="post-title-link" itemprop="url">CVPR2020 | 基于图卷积GNN的多目标跟踪算法解析</a></h2><div class="post-meta"><span class="post-time"><span class="post-meta-item-icon"><i class="fa fa-calendar-o"></i> </span><span class="post-meta-item-text">发表于</span> <time title="创建时间：2020-06-26 23:55:00" itemprop="dateCreated datePublished" datetime="2020-06-26T23:55:00+08:00">2020-06-26</time> </span><span class="post-category"><span class="post-meta-divider">|</span> <span class="post-meta-item-icon"><i class="fa fa-folder-o"></i> </span><span class="post-meta-item-text">分类于</span> <span itemprop="about" itemscope itemtype="http://schema.org/Thing"><a href="/categories/机器学习/" itemprop="url" rel="index"><span itemprop="name">机器学习</span></a></span> ， <span itemprop="about" itemscope itemtype="http://schema.org/Thing"><a href="/categories/机器学习/目标跟踪/" itemprop="url" rel="index"><span itemprop="name">目标跟踪</span></a></span> </span><span class="post-comments-count"><span class="post-meta-divider">|</span> <span class="post-meta-item-icon"><i class="fa fa-comment-o"></i> </span><a href="/2020/06/26/基于GNN的多目标跟踪算法解析/#comments" itemprop="discussionUrl"><span class="post-meta-item-text">评论数：</span> <span class="post-comments-count valine-comment-count" data-xid="/2020/06/26/基于GNN的多目标跟踪算法解析/" itemprop="commentCount"></span></a></span><div class="post-symbolscount"><span class="post-meta-item-icon"><i class="fa fa-file-word-o"></i> </span><span class="post-meta-item-text">本文字数：</span><span title="本文字数">2.9k字</span></div></div></header><div class="post-body" itemprop="articleBody"><h2 id="前言"><a href="#前言" class="headerlink" title="前言"></a>前言</h2><blockquote><p>随着这两年GNN的发展，其对于关系的建模特性也被引入了多目标跟踪领域，这次我通过对这两年基于GNN的MOT算法的介绍来分析其特点。相关MOT和数据关联的基础知识可以去我的<a href="https://zhuanlan.zhihu.com/c_1212750151892365312">专栏</a>查看。</p></blockquote><div class="post-button text-center"><a class="btn" href="/2020/06/26/基于GNN的多目标跟踪算法解析/#more" rel="contents">阅读全文 »</a></div></div><div></div><footer class="post-footer"><div class="post-eof"></div></footer></div></article><article class="post post-type-normal" itemscope itemtype="http://schema.org/Article"><div class="post-block"><link itemprop="mainEntityOfPage" href="https://huangpiao.tech/2020/04/17/arXiv04.17论文解读/"><span hidden itemprop="author" itemscope itemtype="http://schema.org/Person"><meta itemprop="name" content="黄飘"><meta itemprop="description" content="直到这一刻微笑着说话为止，我至少留下了一公升眼泪"><meta itemprop="image" content="/images/author.jpg"></span><span hidden itemprop="publisher" itemscope itemtype="http://schema.org/Organization"><meta itemprop="name" content="见渊の博客"></span><header class="post-header"><h2 class="post-title" itemprop="name headline"><a href="/2020/04/17/arXiv04.17论文解读/" class="post-title-link" itemprop="url">arXiv 04.17 MOT论文解读</a></h2><div class="post-meta"><span class="post-time"><span class="post-meta-item-icon"><i class="fa fa-calendar-o"></i> </span><span class="post-meta-item-text">发表于</span> <time title="创建时间：2020-04-17 15:55:00" itemprop="dateCreated datePublished" datetime="2020-04-17T15:55:00+08:00">2020-04-17</time> </span><span class="post-category"><span class="post-meta-divider">|</span> <span class="post-meta-item-icon"><i class="fa fa-folder-o"></i> </span><span class="post-meta-item-text">分类于</span> <span itemprop="about" itemscope itemtype="http://schema.org/Thing"><a href="/categories/机器学习/" itemprop="url" rel="index"><span itemprop="name">机器学习</span></a></span> ， <span itemprop="about" itemscope itemtype="http://schema.org/Thing"><a href="/categories/机器学习/目标跟踪/" itemprop="url" rel="index"><span itemprop="name">目标跟踪</span></a></span> </span><span class="post-comments-count"><span class="post-meta-divider">|</span> <span class="post-meta-item-icon"><i class="fa fa-comment-o"></i> </span><a href="/2020/04/17/arXiv04.17论文解读/#comments" itemprop="discussionUrl"><span class="post-meta-item-text">评论数：</span> <span class="post-comments-count valine-comment-count" data-xid="/2020/04/17/arXiv04.17论文解读/" itemprop="commentCount"></span></a></span><div class="post-symbolscount"><span class="post-meta-item-icon"><i class="fa fa-file-word-o"></i> </span><span class="post-meta-item-text">本文字数：</span><span title="本文字数">1.9k字</span></div></div></header><div class="post-body" itemprop="articleBody"><h2 id="前言"><a href="#前言" class="headerlink" title="前言"></a>前言</h2><blockquote><p>最近MOT相关的论文出来得很多，4.17又出来了三篇，各有各的特点吧，其中最后一篇我觉得很有意思，我这里一起介绍一些各自的创新之处。</p></blockquote><div class="post-button text-center"><a class="btn" href="/2020/04/17/arXiv04.17论文解读/#more" rel="contents">阅读全文 »</a></div></div><div></div><footer class="post-footer"><div class="post-eof"></div></footer></div></article><article class="post post-type-normal" itemscope itemtype="http://schema.org/Article"><div class="post-block"><link itemprop="mainEntityOfPage" href="https://huangpiao.tech/2020/04/15/基于深度学习的多目标跟踪算法（上）——端到端的数据关联/"><span hidden itemprop="author" itemscope itemtype="http://schema.org/Person"><meta itemprop="name" content="黄飘"><meta itemprop="description" content="直到这一刻微笑着说话为止，我至少留下了一公升眼泪"><meta itemprop="image" content="/images/author.jpg"></span><span hidden itemprop="publisher" itemscope itemtype="http://schema.org/Organization"><meta itemprop="name" content="见渊の博客"></span><header class="post-header"><h2 class="post-title" itemprop="name headline"><a href="/2020/04/15/基于深度学习的多目标跟踪算法（上）——端到端的数据关联/" class="post-title-link" itemprop="url">基于深度学习的多目标跟踪算法（上）——端到端的数据关联</a></h2><div class="post-meta"><span class="post-time"><span class="post-meta-item-icon"><i class="fa fa-calendar-o"></i> </span><span class="post-meta-item-text">发表于</span> <time title="创建时间：2020-04-15 17:35:00" itemprop="dateCreated datePublished" datetime="2020-04-15T17:35:00+08:00">2020-04-15</time> </span><span class="post-category"><span class="post-meta-divider">|</span> <span class="post-meta-item-icon"><i class="fa fa-folder-o"></i> </span><span class="post-meta-item-text">分类于</span> <span itemprop="about" itemscope itemtype="http://schema.org/Thing"><a href="/categories/机器学习/" itemprop="url" rel="index"><span itemprop="name">机器学习</span></a></span> ， <span itemprop="about" itemscope itemtype="http://schema.org/Thing"><a href="/categories/机器学习/目标跟踪/" itemprop="url" rel="index"><span itemprop="name">目标跟踪</span></a></span> </span><span class="post-comments-count"><span class="post-meta-divider">|</span> <span class="post-meta-item-icon"><i class="fa fa-comment-o"></i> </span><a href="/2020/04/15/基于深度学习的多目标跟踪算法（上）——端到端的数据关联/#comments" itemprop="discussionUrl"><span class="post-meta-item-text">评论数：</span> <span class="post-comments-count valine-comment-count" data-xid="/2020/04/15/基于深度学习的多目标跟踪算法（上）——端到端的数据关联/" itemprop="commentCount"></span></a></span><div class="post-symbolscount"><span class="post-meta-item-icon"><i class="fa fa-file-word-o"></i> </span><span class="post-meta-item-text">本文字数：</span><span title="本文字数">5.4k字</span></div></div></header><div class="post-body" itemprop="articleBody"><h2 id="前言"><a href="#前言" class="headerlink" title="前言"></a>前言</h2><blockquote><p>最近基于深度学习的多目标跟踪算法越来越多，有用于特征提取的，有改进单目标跟踪器的，也有提升数据关联的。如果真的要总结的话那就太多了，所以我准备分类别进行介绍，这次我主要介绍端到端的数据关联方法。其中<strong>ICCV2019Tracktor++</strong>的作者团队在<strong>CVPR2020</strong>上被录用的<strong>DeepMOT</strong>和<strong>MPN Tracker</strong>两篇就是专门研究端到端数据关联算法的，这次我们结合近两年的顶会文章进行讲解。后续的部分也会继续在我的专栏更新~</p></blockquote><div class="post-button text-center"><a class="btn" href="/2020/04/15/基于深度学习的多目标跟踪算法（上）——端到端的数据关联/#more" rel="contents">阅读全文 »</a></div></div><div></div><footer class="post-footer"><div class="post-eof"></div></footer></div></article><article class="post post-type-normal" itemscope itemtype="http://schema.org/Article"><div class="post-block"><link itemprop="mainEntityOfPage" href="https://huangpiao.tech/2020/04/08/基于深度学习的多目标跟踪算法（中）——从UMA Tracker(CVPR2020)出发谈谈SOT类MOT算法/"><span hidden itemprop="author" itemscope itemtype="http://schema.org/Person"><meta itemprop="name" content="黄飘"><meta itemprop="description" content="直到这一刻微笑着说话为止，我至少留下了一公升眼泪"><meta itemprop="image" content="/images/author.jpg"></span><span hidden itemprop="publisher" itemscope itemtype="http://schema.org/Organization"><meta itemprop="name" content="见渊の博客"></span><header class="post-header"><h2 class="post-title" itemprop="name headline"><a href="/2020/04/08/基于深度学习的多目标跟踪算法（中）——从UMA Tracker(CVPR2020)出发谈谈SOT类MOT算法/" class="post-title-link" itemprop="url">基于深度学习的多目标跟踪算法（中）——从UMA Tracker(CVPR2020)出发谈谈SOT类MOT算法</a></h2><div class="post-meta"><span class="post-time"><span class="post-meta-item-icon"><i class="fa fa-calendar-o"></i> </span><span class="post-meta-item-text">发表于</span> <time title="创建时间：2020-04-08 22:55:00" itemprop="dateCreated datePublished" datetime="2020-04-08T22:55:00+08:00">2020-04-08</time> <span class="post-meta-divider">|</span> <span class="post-meta-item-icon"><i class="fa fa-calendar-check-o"></i> </span><span class="post-meta-item-text">更新于</span> <time title="修改时间：2020-04-06 22:55:00" itemprop="dateModified" datetime="2020-04-06T22:55:00+08:00">2020-04-06</time> </span><span class="post-category"><span class="post-meta-divider">|</span> <span class="post-meta-item-icon"><i class="fa fa-folder-o"></i> </span><span class="post-meta-item-text">分类于</span> <span itemprop="about" itemscope itemtype="http://schema.org/Thing"><a href="/categories/机器学习/" itemprop="url" rel="index"><span itemprop="name">机器学习</span></a></span> ， <span itemprop="about" itemscope itemtype="http://schema.org/Thing"><a href="/categories/机器学习/目标跟踪/" itemprop="url" rel="index"><span itemprop="name">目标跟踪</span></a></span> </span><span class="post-comments-count"><span class="post-meta-divider">|</span> <span class="post-meta-item-icon"><i class="fa fa-comment-o"></i> </span><a href="/2020/04/08/基于深度学习的多目标跟踪算法（中）——从UMA Tracker(CVPR2020)出发谈谈SOT类MOT算法/#comments" itemprop="discussionUrl"><span class="post-meta-item-text">评论数：</span> <span class="post-comments-count valine-comment-count" data-xid="/2020/04/08/基于深度学习的多目标跟踪算法（中）——从UMA Tracker(CVPR2020)出发谈谈SOT类MOT算法/" itemprop="commentCount"></span></a></span><div class="post-symbolscount"><span class="post-meta-item-icon"><i class="fa fa-file-word-o"></i> </span><span class="post-meta-item-text">本文字数：</span><span title="本文字数">2.9k字</span></div></div></header><div class="post-body" itemprop="articleBody"><h2 id="前言"><a href="#前言" class="headerlink" title="前言"></a>前言</h2><blockquote><p>之前的博客中我们介绍了联合检测和跟踪的多目标跟踪框架，这类框架最大优势在于可以利用优秀的检测器平衡不同观测输入的质量。随之又介绍了端到端的数据关联类算法，这类算法的优势在于可以利用MOT数据信息缓解人工提取特征和计算特征距离的弊端。这次我们要介绍的是基于单目标跟踪（SOT）算法的MOT算法，这类算法的优缺点可以看我下面的介绍。</p></blockquote><div class="post-button text-center"><a class="btn" href="/2020/04/08/基于深度学习的多目标跟踪算法（中）——从UMA Tracker(CVPR2020)出发谈谈SOT类MOT算法/#more" rel="contents">阅读全文 »</a></div></div><div></div><footer class="post-footer"><div class="post-eof"></div></footer></div></article><article class="post post-type-normal" itemscope itemtype="http://schema.org/Article"><div class="post-block"><link itemprop="mainEntityOfPage" href="https://huangpiao.tech/2020/04/08/CVPR2020 多目标跟踪（车辆）与检测框架 RetinaTrack/"><span hidden itemprop="author" itemscope itemtype="http://schema.org/Person"><meta itemprop="name" content="黄飘"><meta itemprop="description" content="直到这一刻微笑着说话为止，我至少留下了一公升眼泪"><meta itemprop="image" content="/images/author.jpg"></span><span hidden itemprop="publisher" itemscope itemtype="http://schema.org/Organization"><meta itemprop="name" content="见渊の博客"></span><header class="post-header"><h2 class="post-title" itemprop="name headline"><a href="/2020/04/08/CVPR2020 多目标跟踪（车辆）与检测框架 RetinaTrack/" class="post-title-link" itemprop="url">CVPR2020 | 多目标跟踪（车辆）与检测框架 RetinaTrack</a></h2><div class="post-meta"><span class="post-time"><span class="post-meta-item-icon"><i class="fa fa-calendar-o"></i> </span><span class="post-meta-item-text">发表于</span> <time title="创建时间：2020-04-08 15:00:00" itemprop="dateCreated datePublished" datetime="2020-04-08T15:00:00+08:00">2020-04-08</time> </span><span class="post-category"><span class="post-meta-divider">|</span> <span class="post-meta-item-icon"><i class="fa fa-folder-o"></i> </span><span class="post-meta-item-text">分类于</span> <span itemprop="about" itemscope itemtype="http://schema.org/Thing"><a href="/categories/机器学习/" itemprop="url" rel="index"><span itemprop="name">机器学习</span></a></span> ， <span itemprop="about" itemscope itemtype="http://schema.org/Thing"><a href="/categories/机器学习/目标跟踪/" itemprop="url" rel="index"><span itemprop="name">目标跟踪</span></a></span> </span><span class="post-comments-count"><span class="post-meta-divider">|</span> <span class="post-meta-item-icon"><i class="fa fa-comment-o"></i> </span><a href="/2020/04/08/CVPR2020 多目标跟踪（车辆）与检测框架 RetinaTrack/#comments" itemprop="discussionUrl"><span class="post-meta-item-text">评论数：</span> <span class="post-comments-count valine-comment-count" data-xid="/2020/04/08/CVPR2020 多目标跟踪（车辆）与检测框架 RetinaTrack/" 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href="https://huangpiao.tech/2020/04/07/MOT开源实时新SOTA A Simple Baseline for Multi-Object Tracking/"><span hidden itemprop="author" itemscope itemtype="http://schema.org/Person"><meta itemprop="name" content="黄飘"><meta itemprop="description" content="直到这一刻微笑着说话为止，我至少留下了一公升眼泪"><meta itemprop="image" content="/images/author.jpg"></span><span hidden itemprop="publisher" itemscope itemtype="http://schema.org/Organization"><meta itemprop="name" content="见渊の博客"></span><header class="post-header"><h2 class="post-title" itemprop="name headline"><a href="/2020/04/07/MOT开源实时新SOTA A Simple Baseline for Multi-Object Tracking/" class="post-title-link" itemprop="url">MOT开源实时新SOTA |A Simple Baseline for Multi-Object Tracking</a></h2><div class="post-meta"><span class="post-time"><span class="post-meta-item-icon"><i class="fa fa-calendar-o"></i> </span><span class="post-meta-item-text">发表于</span> <time title="创建时间：2020-04-07 13:00:00" itemprop="dateCreated datePublished" datetime="2020-04-07T13:00:00+08:00">2020-04-07</time> </span><span class="post-category"><span class="post-meta-divider">|</span> <span class="post-meta-item-icon"><i class="fa fa-folder-o"></i> </span><span class="post-meta-item-text">分类于</span> <span itemprop="about" itemscope itemtype="http://schema.org/Thing"><a href="/categories/机器学习/" itemprop="url" rel="index"><span itemprop="name">机器学习</span></a></span> ， <span itemprop="about" itemscope itemtype="http://schema.org/Thing"><a href="/categories/机器学习/目标跟踪/" itemprop="url" rel="index"><span itemprop="name">目标跟踪</span></a></span> </span><span class="post-comments-count"><span class="post-meta-divider">|</span> <span class="post-meta-item-icon"><i class="fa fa-comment-o"></i> </span><a href="/2020/04/07/MOT开源实时新SOTA A Simple Baseline for Multi-Object Tracking/#comments" itemprop="discussionUrl"><span class="post-meta-item-text">评论数：</span> <span class="post-comments-count valine-comment-count" data-xid="/2020/04/07/MOT开源实时新SOTA A Simple Baseline for Multi-Object Tracking/" itemprop="commentCount"></span></a></span><div class="post-symbolscount"><span class="post-meta-item-icon"><i class="fa fa-file-word-o"></i> </span><span class="post-meta-item-text">本文字数：</span><span title="本文字数">753字</span></div></div></header><div class="post-body" itemprop="articleBody"><hr><h2 id="前言"><a href="#前言" class="headerlink" title="前言"></a>前言</h2><blockquote><p>今天又开源了一篇MOT的新SOTA，也是实时的，也是CenterNet为底层的，估计是看到CenterTrack开源了。emmm….看来我近期看的几篇都在今年某顶会扎堆了，噗。这里我还是把这篇文章给介绍一下吧，有意思的是其中的大部分论点我都在之前的博客([2]、[3])说过了。</p></blockquote><div class="post-button text-center"><a class="btn" href="/2020/04/07/MOT开源实时新SOTA A Simple Baseline for Multi-Object Tracking/#more" rel="contents">阅读全文 »</a></div></div><div></div><footer class="post-footer"><div class="post-eof"></div></footer></div></article><article class="post post-type-normal" itemscope itemtype="http://schema.org/Article"><div class="post-block"><link itemprop="mainEntityOfPage" href="https://huangpiao.tech/2020/04/06/CVPR2020 商汤再谈目标检测中的分类和定位冲突问题/"><span hidden itemprop="author" itemscope itemtype="http://schema.org/Person"><meta itemprop="name" content="黄飘"><meta itemprop="description" content="直到这一刻微笑着说话为止，我至少留下了一公升眼泪"><meta itemprop="image" content="/images/author.jpg"></span><span hidden itemprop="publisher" itemscope itemtype="http://schema.org/Organization"><meta itemprop="name" content="见渊の博客"></span><header class="post-header"><h2 class="post-title" itemprop="name headline"><a href="/2020/04/06/CVPR2020 商汤再谈目标检测中的分类和定位冲突问题/" class="post-title-link" itemprop="url">CVPR2020 商汤|再谈目标检测中的分类和定位冲突问题</a></h2><div class="post-meta"><span class="post-time"><span class="post-meta-item-icon"><i class="fa fa-calendar-o"></i> </span><span class="post-meta-item-text">发表于</span> <time title="创建时间：2020-04-06 23:00:00" itemprop="dateCreated datePublished" datetime="2020-04-06T23:00:00+08:00">2020-04-06</time> </span><span class="post-category"><span 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class="fa fa-file-word-o"></i> </span><span class="post-meta-item-text">本文字数：</span><span title="本文字数">1.6k字</span></div></div></header><div class="post-body" itemprop="articleBody"><hr><h2 id="前言"><a href="#前言" class="headerlink" title="前言"></a>前言</h2><blockquote><p>前段时间我在专栏里详细分析了目标检测中的特征冲突与不对齐问题，今天无意间又看到了商汤在CVPR2020的一篇相关论文，分析角度跟我之前所说的类似，但是解决方案增加了一些技巧，论文中提到其对于各类backbone都有~3%mAP的提升，该算法也用到了OpenImage比赛中，是对Decoupling Head框架的详细分析。这里我们一起来分析下论文内容。</p></blockquote><div class="post-button text-center"><a class="btn" href="/2020/04/06/CVPR2020 商汤再谈目标检测中的分类和定位冲突问题/#more" rel="contents">阅读全文 »</a></div></div><div></div><footer class="post-footer"><div class="post-eof"></div></footer></div></article><article class="post post-type-normal" itemscope itemtype="http://schema.org/Article"><div class="post-block"><link itemprop="mainEntityOfPage" href="https://huangpiao.tech/2020/04/03/联合检测和跟踪的MOT算法解析/"><span hidden itemprop="author" itemscope itemtype="http://schema.org/Person"><meta itemprop="name" content="黄飘"><meta itemprop="description" content="直到这一刻微笑着说话为止，我至少留下了一公升眼泪"><meta itemprop="image" content="/images/author.jpg"></span><span hidden itemprop="publisher" itemscope itemtype="http://schema.org/Organization"><meta itemprop="name" content="见渊の博客"></span><header class="post-header"><h2 class="post-title" itemprop="name headline"><a href="/2020/04/03/联合检测和跟踪的MOT算法解析/" class="post-title-link" itemprop="url">联合检测和跟踪的MOT算法解析（含MOT17 No.1等多个榜前算法!）</a></h2><div class="post-meta"><span class="post-time"><span class="post-meta-item-icon"><i class="fa fa-calendar-o"></i> </span><span class="post-meta-item-text">发表于</span> <time title="创建时间：2020-04-03 17:35:00" itemprop="dateCreated datePublished" datetime="2020-04-03T17:35:00+08:00">2020-04-03</time> </span><span class="post-category"><span class="post-meta-divider">|</span> <span class="post-meta-item-icon"><i class="fa fa-folder-o"></i> </span><span class="post-meta-item-text">分类于</span> <span itemprop="about" itemscope 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class="headerlink" title="前言"></a>前言</h2><blockquote><p>最近一年里，随着Tracktor++这类集成检测和多目标跟踪算法框架的出现，涌现了很多相关的多目标跟踪算法变种，基本都位列MOT Challeng榜单前列，包括刚刚开源的榜首CenterTrack。这里我就对集成检测和跟踪的框架进行分析，相关MOT和数据关联的基础知识可以去我的<a href="https://zhuanlan.zhihu.com/c_1212750151892365312">专栏</a>查看，后期我也会针对基于深度学习的数据关联、ReID2MOT和SOT2MOT等进行专题介绍。</p></blockquote><div class="post-button text-center"><a class="btn" href="/2020/04/03/联合检测和跟踪的MOT算法解析/#more" rel="contents">阅读全文 »</a></div></div><div></div><footer class="post-footer"><div class="post-eof"></div></footer></div></article></section><nav class="pagination"><span class="page-number current">1</span><a class="page-number" href="/page/2/">2</a><span class="space">…</span><a class="page-number" href="/page/5/">5</a><a class="extend next" rel="next" href="/page/2/"><i class="fa fa-angle-right" aria-label="下一页"></i></a></nav></div></div><div class="sidebar-toggle"><div class="sidebar-toggle-line-wrap"><span class="sidebar-toggle-line sidebar-toggle-line-first"></span> <span 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