在血管成形术的临床程序中（即开放式堵塞冠状动脉），在X射线荧光镜检查的指导下，需要将气球和支架等装置（例如气球和支架）放置在动脉中。由于X射线剂量的局限性，所得图像通常是嘈杂的。为了检查这些设备的正确放置，平均进行了多个运动补偿帧以增强视图。因此，设备跟踪是为此目的的必要过程。即使设计为具有易于跟踪的放射性标记的血管成形术设备，但由于标记尺寸较小和血管成形术中的复杂场景，当前的方法难以提供令人满意的结果。在本文中，我们提出了一个用于单个支架跟踪的端到端深度学习框架，该框架由三个层次模块组成：基于U-NET的Landmark检测，基于重新连接的支架提案和功能提取，以及图形卷积神经网络（GCN）基于暂时聚集空间信息和外观特征的支架跟踪。实验表明，与基于点的跟踪模型相比，我们的方法在检测中的性能明显更好。此外，其快速推理速度满足临床要求。

我们提出了一种新颖的形状意识的关系网络，用于内窥镜粘膜颌下粘膜释放（ESD）手术中的准确和实时地标检测。这项任务具有很大的临床意义，但由于复杂的手术环境中出血，照明反射和运动模糊而极其挑战。与现有解决方案相比，通过使用复杂的聚合方案忽略靶向对象之间的几何关系或捕获关系，所提出的网络能够实现令人满意的精度，同时通过充分利用地标之间的空间关系来保持实时性能。我们首先设计一种算法来自动生成关系关键点热量表，其能够直观地代表地标之间的空间关系的先验知识，而无需使用任何额外的手动注释工作。然后，我们开发两个互补正规计划，以逐步将先验知识纳入培训过程。虽然一个方案通过多任务学习引入像素级正则化，但另一个方案通过利用新设计的分组的一致性评估器来实现全局级正则化，该评估将关系约束以越野方式添加到所提出的网络。这两个方案都有利于训练模型，并且可以随时推动才能卸载，以实现实时检测。我们建立了一个大型内部数据集的ESD手术，用于食管癌，以验证我们提出的方法的有效性。广泛的实验结果表明，我们的方法在准确性和效率方面优于最先进的方法，更快地实现了更好的检测结果。在两个下游应用的有希望的结果进一步证实了我们在ESD临床实践中的方法的巨大潜力。

宽阔的区域运动图像（瓦米）产生具有大量极小物体的高分辨率图像。目标物体在连续帧中具有大的空间位移。令人讨厌的图像的这种性质使对象跟踪和检测具有挑战性。在本文中，我们介绍了我们基于深度神经网络的组合对象检测和跟踪模型，即热图网络（HM-Net）。 HM-Net明显快于最先进的帧差异和基于背景减法的方法，而不会影响检测和跟踪性能。 HM-Net遵循基于对象的联合检测和跟踪范式。简单的热图的预测支持无限数量的同时检测。所提出的方法使用来自前一帧的两个连续帧和物体检测热图作为输入，这有助于帧之间的HM-Net监视器时空变化并跟踪先前预测的对象。尽管重复使用先前的物体检测热图作为基于生命的反馈的存储器元件，但它可能导致假阳性检测的意外浪涌。为了增加对误报和消除低置信度检测的方法的稳健性，HM-Net采用新的反馈滤波器和高级数据增强。 HM-Net优于最先进的WAMI移动对象检测和跟踪WPAFB数据集的跟踪方法，其96.2％F1和94.4％地图检测分数，同时在同一数据集上实现61.8％的地图跟踪分数。这种性能对应于F1，6.1％的地图分数的增长率为2.1％，而在追踪最先进的地图分数的地图分数为9.5％。

Tracking has traditionally been the art of following interest points through space and time. This changed with the rise of powerful deep networks. Nowadays, tracking is dominated by pipelines that perform object detection followed by temporal association, also known as tracking-by-detection. We present a simultaneous detection and tracking algorithm that is simpler, faster, and more accurate than the state of the art. Our tracker, CenterTrack, applies a detection model to a pair of images and detections from the prior frame. Given this minimal input, CenterTrack localizes objects and predicts their associations with the previous frame. That's it. CenterTrack is simple, online (no peeking into the future), and real-time. It achieves 67.8% MOTA on the MOT17 challenge at 22 FPS and 89.4% MOTA on the KITTI tracking benchmark at 15 FPS, setting a new state of the art on both datasets. CenterTrack is easily extended to monocular 3D tracking by regressing additional 3D attributes. Using monocular video input, it achieves 28.3% AMOTA@0.2 on the newly released nuScenes 3D tracking benchmark, substantially outperforming the monocular baseline on this benchmark while running at 28 FPS.

解剖标志的本地化对于临床诊断，治疗计划和研究至关重要。在本文中，我们提出了一种新的深网络，名为特征聚合和细化网络（Farnet），用于自动检测解剖标记。为了减轻医疗领域的培训数据有限的问题，我们的网络采用了在自然图像上预先培训的深网络，因为骨干网络和几个流行的网络进行了比较。我们的FARNET还包括多尺度特征聚合模块，用于多尺度特征融合和用于高分辨率热图回归的特征精制模块。粗细的监督应用于两个模块，以方便端到端培训。我们进一步提出了一种名为指数加权中心损耗的新型损失函数，用于准确的热爱回归，这侧重于地标附近的像素的损失并抑制了远处的损失。我们的网络已经在三个公开的解剖学地标检测数据集中进行了评估，包括头部测量射线照片，手射线照片和脊柱射线照相，并在所有三个数据集上实现最先进的性能。代码可用：\ url {https://github.com/juvenileinwind/farnet}

基于无人机（UAV）基于无人机的视觉对象跟踪已实现了广泛的应用，并且由于其多功能性和有效性而引起了智能运输系统领域的越来越多的关注。作为深度学习革命性趋势的新兴力量，暹罗网络在基于无人机的对象跟踪中闪耀，其准确性，稳健性和速度有希望的平衡。由于开发了嵌入式处理器和深度神经网络的逐步优化，暹罗跟踪器获得了广泛的研究并实现了与无人机的初步组合。但是，由于无人机在板载计算资源和复杂的现实情况下，暹罗网络的空中跟踪仍然在许多方面都面临严重的障碍。为了进一步探索基于无人机的跟踪中暹罗网络的部署，这项工作对前沿暹罗跟踪器进行了全面的审查，以及使用典型的无人机板载处理器进行评估的详尽无人用分析。然后，进行板载测试以验证代表性暹罗跟踪器在现实世界无人机部署中的可行性和功效。此外，为了更好地促进跟踪社区的发展，这项工作分析了现有的暹罗跟踪器的局限性，并进行了以低弹片评估表示的其他实验。最后，深入讨论了基于无人机的智能运输系统的暹罗跟踪的前景。领先的暹罗跟踪器的统一框架，即代码库及其实验评估的结果，请访问https://github.com/vision4robotics/siamesetracking4uav。

近年来，多个对象跟踪引起了研究人员的极大兴趣，它已成为计算机视觉中的趋势问题之一，尤其是随着自动驾驶的最新发展。 MOT是针对不同问题的关键视觉任务之一，例如拥挤的场景中的闭塞，相似的外观，小物体检测难度，ID切换等，以应对这些挑战，因为研究人员试图利用变压器的注意力机制，与田径的相互关系，与田径的相互关系，图形卷积神经网络，与暹罗网络不同帧中对象的外观相似性，他们还尝试了基于IOU匹配的CNN网络，使用LSTM的运动预测。为了将这些零散的技术在雨伞下采用，我们研究了过去三年发表的一百多篇论文，并试图提取近代研究人员更关注的技术来解决MOT的问题。我们已经征集了许多应用，可能性以及MOT如何与现实生活有关。我们的评论试图展示研究人员使用过时的技术的不同观点，并为潜在的研究人员提供了一些未来的方向。此外，我们在这篇评论中包括了流行的基准数据集和指标。

这项研究提出了一种基于深度学习的超声（US）图像引导放射疗法的跟踪方法。拟议的级联深度学习模型由注意力网络，基于掩模区域的卷积神经网络（Mask R-CNN）和长期短期记忆（LSTM）网络组成。注意网络从美国图像到可疑的具有里程碑意义的运动区域，以减少搜索区域。然后，面膜R-CNN在减少区域中产生多个利益区域（ROI）建议，并通过三个网络头确定拟议的地标：边界框回归，提案分类和地标分段。 LSTM网络对连续的图像框架之间的时间关系建模，以进行边界框回归和建议分类。为了合并最终建议，根据顺序框架之间的相似性设计选择方法。该方法在肝脏美国跟踪数据集中测试了医疗图像计算和计算机辅助干预措施（MICCAI）2015年的挑战，其中有三位经验丰富的观察者注释了地标，以获得其平均位置。在24个鉴于我们具有地面真相的序列的24个序列上，所有地标的平均跟踪误差为0.65 +/- 0.56毫米，所有地标的误差均在2 mm之内。我们进一步测试了从测试数据集中的69个地标上提出的模型，该模型具有与训练模式相似的图像模式，从而导致平均跟踪误差为0.94 +/- 0.83 mm。我们的实验结果表明，我们提出的方法使用US图像跟踪肝解剖学地标的可行性和准确性，为放射治疗期间的主动运动管理提供了潜在的解决方案。

图提供了一种自然的方式来制定多个对象跟踪（MOT）和多个对象跟踪和分割（MOTS），逐个检测范式中。但是，他们还引入了学习方法的主要挑战，因为定义可以在这种结构化领域运行的模型并不是微不足道的。在这项工作中，我们利用MOT的经典网络流程公式来定义基于消息传递网络（MPN）的完全微分框架。通过直接在图形域上操作，我们的方法可以在整个检测和利用上下文特征上全球推理。然后，它共同预测了数据关联问题的最终解决方案和场景中所有对象的分割掩码，同时利用这两个任务之间的协同作用。我们在几个公开可用的数据集中获得跟踪和细分的最新结果。我们的代码可在github.com/ocetintas/mpntrackseg上找到。

The recent trend in multiple object tracking (MOT) is jointly solving detection and tracking, where object detection and appearance feature (or motion) are learned simultaneously. Despite competitive performance, in crowded scenes, joint detection and tracking usually fail to find accurate object associations due to missed or false detections. In this paper, we jointly model counting, detection and re-identification in an end-to-end framework, named CountingMOT, tailored for crowded scenes. By imposing mutual object-count constraints between detection and counting, the CountingMOT tries to find a balance between object detection and crowd density map estimation, which can help it to recover missed detections or reject false detections. Our approach is an attempt to bridge the gap of object detection, counting, and re-Identification. This is in contrast to prior MOT methods that either ignore the crowd density and thus are prone to failure in crowded scenes, or depend on local correlations to build a graphical relationship for matching targets. The proposed MOT tracker can perform online and real-time tracking, and achieves the state-of-the-art results on public benchmarks MOT16 (MOTA of 77.6), MOT17 (MOTA of 78.0%) and MOT20 (MOTA of 70.2%).

地理定位的概念是指确定地球上的某些“实体”的位置的过程，通常使用全球定位系统（GPS）坐标。感兴趣的实体可以是图像，图像序列，视频，卫星图像，甚至图像中可见的物体。由于GPS标记媒体的大规模数据集由于智能手机和互联网而迅速变得可用，而深入学习已经上升以提高机器学习模型的性能能力，因此由于其显着影响而出现了视觉和对象地理定位的领域广泛的应用，如增强现实，机器人，自驾驶车辆，道路维护和3D重建。本文提供了对涉及图像的地理定位的全面调查，其涉及从捕获图像（图像地理定位）或图像内的地理定位对象（对象地理定位）的地理定位的综合调查。我们将提供深入的研究，包括流行算法的摘要，对所提出的数据集的描述以及性能结果的分析来说明每个字段的当前状态。

Single-frame InfraRed Small Target (SIRST) detection has been a challenging task due to a lack of inherent characteristics, imprecise bounding box regression, a scarcity of real-world datasets, and sensitive localization evaluation. In this paper, we propose a comprehensive solution to these challenges. First, we find that the existing anchor-free label assignment method is prone to mislabeling small targets as background, leading to their omission by detectors. To overcome this issue, we propose an all-scale pseudo-box-based label assignment scheme that relaxes the constraints on scale and decouples the spatial assignment from the size of the ground-truth target. Second, motivated by the structured prior of feature pyramids, we introduce the one-stage cascade refinement network (OSCAR), which uses the high-level head as soft proposals for the low-level refinement head. This allows OSCAR to process the same target in a cascade coarse-to-fine manner. Finally, we present a new research benchmark for infrared small target detection, consisting of the SIRST-V2 dataset of real-world, high-resolution single-frame targets, the normalized contrast evaluation metric, and the DeepInfrared toolkit for detection. We conduct extensive ablation studies to evaluate the components of OSCAR and compare its performance to state-of-the-art model-driven and data-driven methods on the SIRST-V2 benchmark. Our results demonstrate that a top-down cascade refinement framework can improve the accuracy of infrared small target detection without sacrificing efficiency. The DeepInfrared toolkit, dataset, and trained models are available at https://github.com/YimianDai/open-deepinfrared to advance further research in this field.

我们介绍了一种名为RobustAbnet的新表检测和结构识别方法，以检测表的边界并从异质文档图像中重建每个表的细胞结构。为了进行表检测，我们建议将Cornernet用作新的区域建议网络来生成更高质量的表建议，以更快的R-CNN，这显着提高了更快的R-CNN的定位准确性以进行表检测。因此，我们的表检测方法仅使用轻巧的RESNET-18骨干网络，在三个公共表检测基准（即CTDAR TRACKA，PUBLAYNET和IIIT-AR-13K）上实现最新性能。此外，我们提出了一种新的基于分裂和合并的表结构识别方法，其中提出了一个新型的基于CNN的新空间CNN分离线预测模块将每个检测到的表分为单元格，并且基于网格CNN的CNN合并模块是应用用于恢复生成细胞。由于空间CNN模块可以有效地在整个表图像上传播上下文信息，因此我们的表结构识别器可以坚固地识别具有较大的空白空间和几何扭曲（甚至弯曲）表的表。得益于这两种技术，我们的表结构识别方法在包括SCITSR，PubTabnet和CTDAR TrackB2-Modern在内的三个公共基准上实现了最先进的性能。此外，我们进一步证明了我们方法在识别具有复杂结构，大空间以及几何扭曲甚至弯曲形状的表上的表格上的优势。

X-ray imaging technology has been used for decades in clinical tasks to reveal the internal condition of different organs, and in recent years, it has become more common in other areas such as industry, security, and geography. The recent development of computer vision and machine learning techniques has also made it easier to automatically process X-ray images and several machine learning-based object (anomaly) detection, classification, and segmentation methods have been recently employed in X-ray image analysis. Due to the high potential of deep learning in related image processing applications, it has been used in most of the studies. This survey reviews the recent research on using computer vision and machine learning for X-ray analysis in industrial production and security applications and covers the applications, techniques, evaluation metrics, datasets, and performance comparison of those techniques on publicly available datasets. We also highlight some drawbacks in the published research and give recommendations for future research in computer vision-based X-ray analysis.

Point cloud learning has lately attracted increasing attention due to its wide applications in many areas, such as computer vision, autonomous driving, and robotics. As a dominating technique in AI, deep learning has been successfully used to solve various 2D vision problems. However, deep learning on point clouds is still in its infancy due to the unique challenges faced by the processing of point clouds with deep neural networks. Recently, deep learning on point clouds has become even thriving, with numerous methods being proposed to address different problems in this area. To stimulate future research, this paper presents a comprehensive review of recent progress in deep learning methods for point clouds. It covers three major tasks, including 3D shape classification, 3D object detection and tracking, and 3D point cloud segmentation. It also presents comparative results on several publicly available datasets, together with insightful observations and inspiring future research directions.

运动估计是用于评估目标器官解剖学和功能的动态医学图像处理的基本步骤。然而，通过评估局部图像相似性通过评估局部图像相似性优化运动场的基于图像的运动估计方法，易于产生令人难以置信的估计，尤其是在大运动的情况下。在这项研究中，我们提供了一种新颖的稀疏密度（DSD）的运动估计框架，其包括两个阶段。在第一阶段，我们处理原始密集图像以提取稀疏地标以表示目标器官解剖拓扑，并丢弃对运动估计不必要的冗余信息。为此目的，我们介绍一个无监督的3D地标检测网络，以提取用于目标器官运动估计的空间稀疏但代表性的地标。在第二阶段，我们从两个不同时间点的两个图像的提取稀疏地标的稀疏运动位移得出。然后，我们通过将稀疏地标位移突出回致密图像域，呈现运动重建网络来构造运动场。此外，我们从我们的两级DSD框架中使用估计的运动场作为初始化，并提高轻量级且有效的迭代优化中的运动估计质量。我们分别评估了两种动态医学成像任务的方法，分别为模型心脏运动和肺呼吸运动。与现有的比较方法相比，我们的方法产生了出色的运动估计精度。此外，广泛的实验结果表明，我们的解决方案可以提取良好代表性解剖标志，而无需手动注释。我们的代码在线公开提供。

Accurate whole-body multi-person pose estimation and tracking is an important yet challenging topic in computer vision. To capture the subtle actions of humans for complex behavior analysis, whole-body pose estimation including the face, body, hand and foot is essential over conventional body-only pose estimation. In this paper, we present AlphaPose, a system that can perform accurate whole-body pose estimation and tracking jointly while running in realtime. To this end, we propose several new techniques: Symmetric Integral Keypoint Regression (SIKR) for fast and fine localization, Parametric Pose Non-Maximum-Suppression (P-NMS) for eliminating redundant human detections and Pose Aware Identity Embedding for jointly pose estimation and tracking. During training, we resort to Part-Guided Proposal Generator (PGPG) and multi-domain knowledge distillation to further improve the accuracy. Our method is able to localize whole-body keypoints accurately and tracks humans simultaneously given inaccurate bounding boxes and redundant detections. We show a significant improvement over current state-of-the-art methods in both speed and accuracy on COCO-wholebody, COCO, PoseTrack, and our proposed Halpe-FullBody pose estimation dataset. Our model, source codes and dataset are made publicly available at https://github.com/MVIG-SJTU/AlphaPose.

Due to object detection's close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable architectures. Their performance easily stagnates by constructing complex ensembles which combine multiple low-level image features with high-level context from object detectors and scene classifiers. With the rapid development in deep learning, more powerful tools, which are able to learn semantic, high-level, deeper features, are introduced to address the problems existing in traditional architectures. These models behave differently in network architecture, training strategy and optimization function, etc. In this paper, we provide a review on deep learning based object detection frameworks. Our review begins with a brief introduction on the history of deep learning and its representative tool, namely Convolutional Neural Network (CNN). Then we focus on typical generic object detection architectures along with some modifications and useful tricks to improve detection performance further. As distinct specific detection tasks exhibit different characteristics, we also briefly survey several specific tasks, including salient object detection, face detection and pedestrian detection. Experimental analyses are also provided to compare various methods and draw some meaningful conclusions. Finally, several promising directions and tasks are provided to serve as guidelines for future work in both object detection and relevant neural network based learning systems.

对象运动和对象外观是多个对象跟踪（MOT）应用中的常用信息，用于将帧跨越帧的检测相关联，或用于联合检测和跟踪方法的直接跟踪预测。然而，不仅是这两种类型的信息通常是单独考虑的，而且它们也没有帮助直接从当前感兴趣帧中使用视觉信息的用法。在本文中，我们提出了PatchTrack，一种基于变压器的联合检测和跟踪系统，其使用当前感兴趣的帧帧的曲线预测曲目。我们使用卡尔曼滤波器从前一帧预测当前帧中的现有轨道的位置。从预测边界框裁剪的补丁被发送到变压器解码器以推断新曲目。通过利用在补丁中编码的对象运动和对象外观信息，所提出的方法将更多地关注新曲目更有可能发生的位置。我们展示了近期MOT基准的Patchtrack的有效性，包括MOT16（MOTA 73.71％，IDF1 65.77％）和MOT17（MOTA 73.59％，IDF1 65.23％）。结果在https://motchallenge.net/method/mot=4725&chl=10上发布。

Existing Multiple Object Tracking (MOT) methods design complex architectures for better tracking performance. However, without a proper organization of input information, they still fail to perform tracking robustly and suffer from frequent identity switches. In this paper, we propose two novel methods together with a simple online Message Passing Network (MPN) to address these limitations. First, we explore different integration methods for the graph node and edge embeddings and put forward a new IoU (Intersection over Union) guided function, which improves long term tracking and handles identity switches. Second, we introduce a hierarchical sampling strategy to construct sparser graphs which allows to focus the training on more difficult samples. Experimental results demonstrate that a simple online MPN with these two contributions can perform better than many state-of-the-art methods. In addition, our association method generalizes well and can also improve the results of private detection based methods.