随着已安装的摄像机的数量，需要处理和分析这些摄像机捕获的所有图像所需的计算资源。视频分析使新用例（例如智能城市）或自动驾驶等开放。与此同时，它敦促服务提供商安装额外的计算资源以应对需求，而严格的延迟要求推动到网络末尾的计算，形成了地理分布式和异构的计算位置集，共享和资源受限。这种景观（共享和分布式位置）迫使我们设计可以在所有可用位置之间优化和分发工作的新技术，并且理想情况下，使得计算要求在安装的相机的数量方面增长。在本文中，我们展示了FOMO（专注于移动物体）。该方法通过预处理场景，过滤空区输出并将来自多个摄像机的感兴趣区域组成为用于预先训练的对象检测模型的输入的单个图像来有效地优化多摄像机部署。结果表明，整体系统性能可以提高8倍，而精度可提高40％作为方法的副产物，所有这些都是使用储物预训练模型，没有额外的训练或微调。

通过流行和通用的计算机视觉挑战来判断，如想象成或帕斯卡VOC，神经网络已经证明是在识别任务中特别准确。然而，最先进的准确性通常以高计算价格出现，需要硬件加速来实现实时性能，而使用案例（例如智能城市）需要实时分析固定摄像机的图像。由于网络带宽的数量，这些流将生成，我们不能依赖于卸载计算到集中云。因此，预期分布式边缘云将在本地处理图像。但是，边缘是由性质资源约束的，这给了可以执行的计算复杂性限制。然而，需要边缘与准确的实时视频分析之间的会面点。专用轻量级型号在每相机基础上可能有所帮助，但由于相机的数量增长，除非该过程是自动的，否则它很快就会变得不可行。在本文中，我们展示并评估COVA（上下文优化的视频分析），这是一个框架，可以帮助在边缘相机中自动专用模型专业化。 COVA通过专业化自动提高轻质模型的准确性。此外，我们讨论和审查过程中涉及的每个步骤，以了解每个人所带来的不同权衡。此外，我们展示了静态相机的唯一假设如何使我们能够制定一系列考虑因素，这大大简化了问题的范围。最后，实验表明，最先进的模型，即能够概括到看不见的环境，可以有效地用作教师以以恒定的计算成本提高较小网络的教师，提高精度。结果表明，我们的COVA可以平均提高预先训练的型号的准确性，平均为21％。

Video, as a key driver in the global explosion of digital information, can create tremendous benefits for human society. Governments and enterprises are deploying innumerable cameras for a variety of applications, e.g., law enforcement, emergency management, traffic control, and security surveillance, all facilitated by video analytics (VA). This trend is spurred by the rapid advancement of deep learning (DL), which enables more precise models for object classification, detection, and tracking. Meanwhile, with the proliferation of Internet-connected devices, massive amounts of data are generated daily, overwhelming the cloud. Edge computing, an emerging paradigm that moves workloads and services from the network core to the network edge, has been widely recognized as a promising solution. The resulting new intersection, edge video analytics (EVA), begins to attract widespread attention. Nevertheless, only a few loosely-related surveys exist on this topic. A dedicated venue for collecting and summarizing the latest advances of EVA is highly desired by the community. Besides, the basic concepts of EVA (e.g., definition, architectures, etc.) are ambiguous and neglected by these surveys due to the rapid development of this domain. A thorough clarification is needed to facilitate a consensus on these concepts. To fill in these gaps, we conduct a comprehensive survey of the recent efforts on EVA. In this paper, we first review the fundamentals of edge computing, followed by an overview of VA. The EVA system and its enabling techniques are discussed next. In addition, we introduce prevalent frameworks and datasets to aid future researchers in the development of EVA systems. Finally, we discuss existing challenges and foresee future research directions. We believe this survey will help readers comprehend the relationship between VA and edge computing, and spark new ideas on EVA.

The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection. The subchallenges were based on the SeaDronesSee and MODS benchmarks. This report summarizes the main findings of the individual subchallenges and introduces a new benchmark, called SeaDronesSee Object Detection v2, which extends the previous benchmark by including more classes and footage. We provide statistical and qualitative analyses, and assess trends in the best-performing methodologies of over 130 submissions. The methods are summarized in the appendix. The datasets, evaluation code and the leaderboard are publicly available at https://seadronessee.cs.uni-tuebingen.de/macvi.

Event-based vision has been rapidly growing in recent years justified by the unique characteristics it presents such as its high temporal resolutions (~1us), high dynamic range (>120dB), and output latency of only a few microseconds. This work further explores a hybrid, multi-modal, approach for object detection and tracking that leverages state-of-the-art frame-based detectors complemented by hand-crafted event-based methods to improve the overall tracking performance with minimal computational overhead. The methods presented include event-based bounding box (BB) refinement that improves the precision of the resulting BBs, as well as a continuous event-based object detection method, to recover missed detections and generate inter-frame detections that enable a high-temporal-resolution tracking output. The advantages of these methods are quantitatively verified by an ablation study using the higher order tracking accuracy (HOTA) metric. Results show significant performance gains resembled by an improvement in the HOTA from 56.6%, using only frames, to 64.1% and 64.9%, for the event and edge-based mask configurations combined with the two methods proposed, at the baseline framerate of 24Hz. Likewise, incorporating these methods with the same configurations has improved HOTA from 52.5% to 63.1%, and from 51.3% to 60.2% at the high-temporal-resolution tracking rate of 384Hz. Finally, a validation experiment is conducted to analyze the real-world single-object tracking performance using high-speed LiDAR. Empirical evidence shows that our approaches provide significant advantages compared to using frame-based object detectors at the baseline framerate of 24Hz and higher tracking rates of up to 500Hz.

在这项工作中，我们呈现了DCC（更深层兼容的压缩），用于实时无人机的辅助边缘辅助视频分析的一个启用技术，内置于现有编解码器之上。DCC解决了一个重要的技术问题，以将流动的视频从无人机压缩到边缘，而不会严格地在边缘执行的视频分析任务的准确性和及时性。DCC通过流式视频中的每一位对视频分析同样有价值，这是对视频分析的同样有价值，这在传统的分析透视技术编解码器技术上打开了新的压缩室。我们利用特定的无人机的上下文和中级提示，从物体检测中追求保留分析质量所需的自适应保真度。我们在一个展示车辆检测应用中有原型DCC，并验证了其代表方案的效率。DCC通过基线方法减少9.5倍，在最先进的检测精度上，19-683％的速度减少了9.5倍。

卫星摄像机可以为大型区域提供连续观察，这对于许多遥感应用很重要。然而，由于对象的外观信息不足和缺乏高质量数据集，在卫星视频中实现移动对象检测和跟踪仍然具有挑战性。在本文中，我们首先构建一个具有丰富注释的大型卫星视频数据集，用于移动对象检测和跟踪的任务。该数据集由Jilin-1卫星星座收集，并由47个高质量视频组成，对象检测有1,646,038兴趣的情况和用于对象跟踪的3,711个轨迹。然后，我们引入运动建模基线，以提高检测速率并基于累积多帧差异和鲁棒矩阵完成来减少误报。最后，我们建立了第一个用于在卫星视频中移动对象检测和跟踪的公共基准，并广泛地评估在我们数据集上几种代表方法的性能。还提供了综合实验分析和富有魅力的结论。数据集可在https://github.com/qingyonghu/viso提供。

每年，AEDESAEGYPTI蚊子都感染了数百万人，如登录，ZIKA，Chikungunya和城市黄热病等疾病。战斗这些疾病的主要形式是通过寻找和消除潜在的蚊虫养殖场来避免蚊子繁殖。在这项工作中，我们介绍了一个全面的空中视频数据集，获得了无人驾驶飞行器，含有可能的蚊帐。使用识别所有感兴趣对象的边界框手动注释视频数据集的所有帧。该数据集被用于开发基于深度卷积网络的这些对象的自动检测系统。我们提出了通过在可以注册检测到的对象的时空检测管道的对象检测流水线中的融合来利用视频中包含的时间信息，这些时间是可以注册检测到的对象的，最大限度地减少最伪正和假阴性的出现。此外，我们通过实验表明使用视频比仅使用框架对马赛克组成马赛克更有利。使用Reset-50-FPN作为骨干，我们可以分别实现0.65和0.77的F $ _1 $ -70分别对“轮胎”和“水箱”的对象级别检测，说明了正确定位潜在蚊子的系统能力育种对象。

在监控和搜索和救援应用程序中，重要的是在低端设备上实时执行多目标跟踪（MOT）。今天的MOT解决方案采用深度神经网络，往往具有高计算复杂性。识别帧大小对跟踪性能的影响，我们提出了深度，一种模型不可知框架尺寸选择方法，可在现有的全卷积网络基跟踪器之上进行操作，以加速跟踪吞吐量。在培训阶段，我们将可检测性分数纳入单次跟踪器架构，使得DeepScale以自我监督的方式学习不同帧大小的表示估计。在推理期间，它可以根据基于用户控制参数根据视觉内容的复杂性来调整帧大小。为了利用边缘服务器上的计算资源，我们提出了两个计算分区模式，即仅使用自适应帧大小传输和边缘服务器辅助跟踪仅适用于MOT，即边缘服务器。 MOT数据集的广泛实验和基准测试证明了深度的有效性和灵活性。与最先进的追踪器相比，DeepScale ++，DeepScale的变种实现1.57倍加速，仅在一个配置中的MOT15数据集上跟踪准确性。我们已经实现和评估了DeepScale ++，以及由NVIDIA JETSON TX2板和GPU服务器组成的小型测试平台上所提出的计算分区方案。实验显示与仅服务器或智能相机的解决方案相比跟踪性能和延迟之间的非琐碎权衡。

边缘计算广泛用于视频分析。为了减轻准确性和成本之间的固有张力，已经提出了各种视频分析管道，以优化GPU在边缘节点上的使用。但是，我们发现，由于视频内容的变化，在管道的不同位置的视频内容变化，亚次采样和过滤，因此为边缘节点提供的GPU计算资源通常被低估了。与模型和管道优化相反，在这项工作中，我们使用非确定性和分散的闲置GPU资源研究了机会数据增强的问题。具体而言，我们提出了一个特定于任务的歧视和增强模块以及一种模型感知的对抗性训练机制，提供了一种以准确有效的方式识别和转换特定于视频管道的低质量图像的方法。在延迟和GPU资源限制下，进一步开发了多个EXIT模型结构和资源感知调度程序，以做出在线增强决策和细粒度的执行。多个视频分析管道和数据集的实验表明，通过明智地分配少量的空闲资源，这些框架上倾向于通过增强而产生更大的边际收益，我们的系统将DNN对象检测准确性提高了7.3-11.3 \％，而不会产生任何潜行成本。

The International Workshop on Reading Music Systems (WoRMS) is a workshop that tries to connect researchers who develop systems for reading music, such as in the field of Optical Music Recognition, with other researchers and practitioners that could benefit from such systems, like librarians or musicologists. The relevant topics of interest for the workshop include, but are not limited to: Music reading systems; Optical music recognition; Datasets and performance evaluation; Image processing on music scores; Writer identification; Authoring, editing, storing and presentation systems for music scores; Multi-modal systems; Novel input-methods for music to produce written music; Web-based Music Information Retrieval services; Applications and projects; Use-cases related to written music. These are the proceedings of the 3rd International Workshop on Reading Music Systems, held in Alicante on the 23rd of July 2021.

水果和蔬菜的检测，分割和跟踪是精确农业的三个基本任务，实现了机器人的收获和产量估计。但是，现代算法是饥饿的数据，并非总是有可能收集足够的数据来运用最佳性能的监督方法。由于数据收集是一项昂贵且繁琐的任务，因此在农业中使用计算机视觉的能力通常是小企业无法实现的。在此背景下的先前工作之后，我们提出了一种初始弱监督的解决方案，以减少在精确农业应用程序中获得最新检测和细分所需的数据，在这里，我们在这里改进该系统并探索跟踪果实的问题果园。我们介绍了拉齐奥南部（意大利）葡萄的葡萄园案例，因为葡萄由于遮挡，颜色和一般照明条件而难以分割。当有一些可以用作源数据的初始标记数据（例如，葡萄酒葡萄数据）时，我们会考虑这种情况，但与目标数据有很大不同（例如表格葡萄数据）。为了改善目标数据的检测和分割，我们建议使用弱边界框标签训练分割算法，而对于跟踪，我们从运动算法中利用3D结构来生成来自已标记样品的新标签。最后，将两个系统组合成完整的半监督方法。与SOTA监督解决方案的比较表明，我们的方法如何能够训练以很少的标记图像和非常简单的标签来实现高性能的新型号。

Visual object tracking under challenging conditions of motion and light can be hindered by the capabilities of conventional cameras, prone to producing images with motion blur. Event cameras are novel sensors suited to robustly perform vision tasks under these conditions. However, due to the nature of their output, applying them to object detection and tracking is non-trivial. In this work, we propose a framework to take advantage of both event cameras and off-the-shelf deep learning for object tracking. We show that reconstructing event data into intensity frames improves the tracking performance in conditions under which conventional cameras fail to provide acceptable results.

Fires have destructive power when they break out and affect their surroundings on a devastatingly large scale. The best way to minimize their damage is to detect the fire as quickly as possible before it has a chance to grow. Accordingly, this work looks into the potential of AI to detect and recognize fires and reduce detection time using object detection on an image stream. Object detection has made giant leaps in speed and accuracy over the last six years, making real-time detection feasible. To our end, we collected and labeled appropriate data from several public sources, which have been used to train and evaluate several models based on the popular YOLOv4 object detector. Our focus, driven by a collaborating industrial partner, is to implement our system in an industrial warehouse setting, which is characterized by high ceilings. A drawback of traditional smoke detectors in this setup is that the smoke has to rise to a sufficient height. The AI models brought forward in this research managed to outperform these detectors by a significant amount of time, providing precious anticipation that could help to minimize the effects of fires further.

空中无人机镜头的视觉检查是当今土地搜索和救援（SAR）运营的一个组成部分。由于此检查是对人类的缓慢而繁琐，令人疑惑的工作，我们提出了一种新颖的深入学习算法来自动化该航空人员检测（APD）任务。我们试验模型架构选择，在线数据增强，转移学习，图像平铺和其他几种技术，以提高我们方法的测试性能。我们将新型航空检验视网膜（空气）算法呈现为这些贡献的结合。空中探测器在精度（〜21个百分点增加）和速度方面，在常用的SAR测试数据上表现出最先进的性能。此外，我们为SAR任务中的APD问题提供了新的正式定义。也就是说，我们提出了一种新的评估方案，在现实世界SAR本地化要求方面排名探测器。最后，我们提出了一种用于稳健的新型后处理方法，近似对象定位：重叠边界框（MOB）算法的合并。在空中检测器中使用的最终处理阶段在真实的空中SAR任务面前显着提高了其性能和可用性。

有效的视觉在延迟预算下的精度最大化。这些作品一次评估脱机准确性，一次是一张图像。但是，诸如自动驾驶之类的实时视觉应用在流媒体设置中运行，在这些设置中，地面真相在推理开始和终点之间会发生变化。这会导致明显的准确性下降。因此，最近提出的一项旨在最大程度地提高流媒体设置准确性的工作。在本文中，我们建议在每个环境环境中最大化流的准确性。我们认为场景难度会影响初始（离线）精度差异，而场景中的障碍物位移会影响后续的准确性降解。我们的方法章鱼使用这些方案属性来选择在测试时最大化流量准确性的配置。我们的方法将跟踪性能（S-MOTA）提高了7.4％，而常规静态方法则提高了。此外，使用我们的方法提高性能，而不是离线准确性的进步，而不是代替而不是进步。

由于捕获的图像中的严重噪音，弱光下的场景推断是一个具有挑战性的问题。减少噪音的一种方法是在捕获过程中使用更长的曝光。但是，在有运动（场景或相机运动）的存在下，较长的暴露会导致运动模糊，从而导致图像信息的丢失。这在这两种图像降解之间创造了权衡取舍：运动模糊（由于长期暴露）与噪声（由于曝光短），也称为本文中的双图像损坏对。随着摄像机的兴起，能够同时捕获同一场景的多次暴露，因此可以克服这一权衡。我们的主要观察结果是，尽管这些不同图像捕获的降解的数量和性质各不相同，但在所有图像中，语义内容保持不变。为此，我们提出了一种方法，以利用这些多曝光捕获在弱光和运动下的鲁棒推理。我们的方法建立在功能一致性损失的基础上，以鼓励这些单个捕获的类似结果，并利用其最终预测的合奏来实现强大的视觉识别。我们证明了方法对模拟图像的有效性以及具有多个暴露的真实捕获，以及对象检测和图像分类的任务。

工业X射线分析在需要保证某些零件的结构完整性的航空航天，汽车或核行业中很常见。但是，射线照相图像的解释有时很困难，可能导致两名专家在缺陷分类上不同意。本文介绍的自动缺陷识别（ADR）系统将减少分析时间，还将有助于减少对缺陷的主观解释，同时提高人类检查员的可靠性。我们的卷积神经网络（CNN）模型达到94.2 \％准确性（MAP@iou = 50 \％），当应用于汽车铝铸件数据集（GDXRAR）时，它被认为与预期的人类性能相似，超过了当前状态该数据集的艺术。在工业环境上，其推理时间少于每个DICOM图像，因此可以安装在生产设施上，不会影响交付时间。此外，还进行了对主要高参数的消融研究，以优化从75 \％映射的初始基线结果最高94.2 \％map的模型准确性。

Computer vision applications in intelligent transportation systems (ITS) and autonomous driving (AD) have gravitated towards deep neural network architectures in recent years. While performance seems to be improving on benchmark datasets, many real-world challenges are yet to be adequately considered in research. This paper conducted an extensive literature review on the applications of computer vision in ITS and AD, and discusses challenges related to data, models, and complex urban environments. The data challenges are associated with the collection and labeling of training data and its relevance to real world conditions, bias inherent in datasets, the high volume of data needed to be processed, and privacy concerns. Deep learning (DL) models are commonly too complex for real-time processing on embedded hardware, lack explainability and generalizability, and are hard to test in real-world settings. Complex urban traffic environments have irregular lighting and occlusions, and surveillance cameras can be mounted at a variety of angles, gather dirt, shake in the wind, while the traffic conditions are highly heterogeneous, with violation of rules and complex interactions in crowded scenarios. Some representative applications that suffer from these problems are traffic flow estimation, congestion detection, autonomous driving perception, vehicle interaction, and edge computing for practical deployment. The possible ways of dealing with the challenges are also explored while prioritizing practical deployment.

The PASCAL Visual Object Classes (VOC) challenge is a benchmark in visual object category recognition and detection, providing the vision and machine learning communities with a standard dataset of images and annotation, and standard evaluation procedures. Organised annually from 2005 to present, the challenge and its associated dataset has become accepted as the benchmark for object detection.This paper describes the dataset and evaluation procedure. We review the state-of-the-art in evaluated methods for both classification and detection, analyse whether the methods are statistically different, what they are learning from the images (e.g. the object or its context), and what the methods find easy or confuse. The paper concludes with lessons learnt in the three year history of the challenge, and proposes directions for future improvement and extension.