当前的多类多类别对象跟踪（MOT）指标使用类标签来分组跟踪结果以进行每类评估。同样，MOT方法通常仅将对象与相同的类预测相关联。这两种MOT中的普遍策略隐含地假设分类性能几乎完美。但是，这远非最近的大型MOT数据集中的情况，这些数据集包含许多罕见或语义上类似类别的类别。因此，所得的不正确分类导致跟踪器的基准跟踪和基准不足。我们通过将分类与跟踪无关，以解决这些问题。我们引入了一个新的指标，跟踪所有准确性（TETA），将跟踪测量测量分为三个子因素：本地化，关联和分类，即使在不准确的分类下，也可以全面地跟踪性能的基准测试。 TETA还处理了大规模跟踪数据集中具有挑战性的不完整注释问题。我们进一步介绍了使用类示例匹配（CEM）执行关联的每件事跟踪器（TETER）。我们的实验表明，TETA对跟踪器进行更全面的评估，并且与最先进的ART相比，TETE对挑战性的大规模数据集BDD100K和TAO进行了重大改进。

多个现有基准测试涉及视频中的跟踪和分割对象，例如，视频对象细分（VOS）和多对象跟踪和分割（MOTS）（MOTS），但是由于使用不同的基准标准数据集和指标，它们之间几乎没有相互作用（例如J＆F，J＆F，J＆F，J＆F，地图，smotsa）。结果，已发表的作品通常针对特定的基准，并且不容易相互媲美。我们认为，可以解决多个任务的广义方法的发展需要在这些研究子社区中更大的凝聚力。在本文中，我们旨在通过提出爆发来促进这一点，该数据集包含数千个带有高质量对象掩码的视频，以及一个相关的基准标准，其中包含六个任务，涉及视频中的对象跟踪和细分。使用相同的数据和可比较的指标对所有任务进行评估，这使研究人员能够一致考虑它们，因此更有效地从不同任务的不同方法中汇集了知识。此外，我们为所有任务展示了几个基线，并证明可以将一个任务的方法应用于另一个任务，并具有可量化且可解释的性能差异。数据集注释和评估代码可在以下网址获得：https：//github.com/ali2500/burst-benchmark。

为视频中的每个像素分配语义类和跟踪身份的任务称为视频Panoptic分段。我们的工作是第一个在真实世界中瞄准这项任务，需要在空间和时间域中的密集解释。由于此任务的地面真理难以获得，但是，现有数据集是合成构造的或仅在短视频剪辑中稀疏地注释。为了克服这一点，我们介绍了一个包含两个数据集，Kitti-Step和Motchallenge步骤的新基准。数据集包含长视频序列，提供具有挑战性的示例和用于研究长期像素精确分割和在真实条件下跟踪的测试床。我们进一步提出了一种新的评估度量分割和跟踪质量（STQ），其相当余额平衡该任务的语义和跟踪方面，并且更适合评估任意长度的序列。最后，我们提供了几个基线来评估此新具有挑战性数据集的现有方法的状态。我们已将我们的数据集，公制，基准服务器和基准公开提供，并希望这将激发未来的研究。

多目标跟踪（MOT）的典型管道是使用探测器进行对象本地化，并在重新识别（RE-ID）之后进行对象关联。该管道通过对象检测和重新ID的最近进展部分而部分地激励，并且部分地通过现有的跟踪数据集中的偏差激励，其中大多数物体倾向于具有区分外观和RE-ID模型足以建立关联。为了响应这种偏见，我们希望重新强调多目标跟踪的方法也应该在对象外观不充分辨别时起作用。为此，我们提出了一个大型数据集，用于多人跟踪，人类具有相似的外观，多样化的运动和极端关节。由于数据集包含主要组跳舞视频，我们将其命名为“DanceTrack”。我们预计DanceTrack可以提供更好的平台，以开发更多的MOT算法，这些算法依赖于视觉识别并更依赖于运动分析。在我们的数据集上，我们在数据集上基准测试了几个最先进的追踪器，并在与现有基准测试中遵守DanceTrack的显着性能下降。 DataSet，项目代码和竞争服务器播放：\ url {https://github.com/danceTrack}。

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.

Scaling object taxonomies is one of the important steps toward a robust real-world deployment of recognition systems. We have faced remarkable progress in images since the introduction of the LVIS benchmark. To continue this success in videos, a new video benchmark, TAO, was recently presented. Given the recent encouraging results from both detection and tracking communities, we are interested in marrying those two advances and building a strong large vocabulary video tracker. However, supervisions in LVIS and TAO are inherently sparse or even missing, posing two new challenges for training the large vocabulary trackers. First, no tracking supervisions are in LVIS, which leads to inconsistent learning of detection (with LVIS and TAO) and tracking (only with TAO). Second, the detection supervisions in TAO are partial, which results in catastrophic forgetting of absent LVIS categories during video fine-tuning. To resolve these challenges, we present a simple but effective learning framework that takes full advantage of all available training data to learn detection and tracking while not losing any LVIS categories to recognize. With this new learning scheme, we show that consistent improvements of various large vocabulary trackers are capable, setting strong baseline results on the challenging TAO benchmarks.

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.

尽管广泛用作可视检测任务的性能措施，但平均精度（AP）In（i）的限制在反映了本地化质量，（ii）对其计算的设计选择的鲁棒性以及其对输出的适用性没有信心分数。 Panoptic质量（PQ），提出评估Panoptic Seationation（Kirillov等，2019）的措施，不会遭受这些限制，而是限于Panoptic Seationation。在本文中，我们提出了基于其本地化和分类质量的视觉检测器的平均匹配误差，提出了定位召回精度（LRP）误差。 LRP错误，最初仅为Oksuz等人进行对象检测。 （2018），不遭受上述限制，适用于所有视觉检测任务。我们还介绍了最佳LRP（OLRP）错误，因为通过置信区获得的最小LRP错误以评估视觉检测器并获得部署的最佳阈值。我们提供对AP和PQ的LRP误差的详细比较分析，并使用七个可视检测任务（即对象检测，关键点检测，实例分割，Panoptic分段，视觉关系检测，使用近100个最先进的视觉检测器零拍摄检测和广义零拍摄检测）使用10个数据集来统一地显示LRP误差提供比其对应物更丰富和更辨别的信息。可用的代码：https：//github.com/kemaloksuz/lrp-error

In this paper we present a new computer vision task, named video instance segmentation. The goal of this new task is simultaneous detection, segmentation and tracking of instances in videos. In words, it is the first time that the image instance segmentation problem is extended to the video domain. To facilitate research on this new task, we propose a large-scale benchmark called YouTube-VIS, which consists of 2,883 high-resolution YouTube videos, a 40-category label set and 131k high-quality instance masks.In addition, we propose a novel algorithm called Mask-Track R-CNN for this task. Our new method introduces a new tracking branch to Mask R-CNN to jointly perform the detection, segmentation and tracking tasks simultaneously. Finally, we evaluate the proposed method and several strong baselines on our new dataset. Experimental results clearly demonstrate the advantages of the proposed algorithm and reveal insight for future improvement. We believe the video instance segmentation task will motivate the community along the line of research for video understanding.

我们介绍了几次视频对象检测（FSVOD），在我们的高度多样化和充满活力的世界中为视觉学习提供了三个贡献：1）大规模视频数据集FSVOD-500，其中包括每个类别中的500个类别，其中少数 - 学习;2）一种新型管建议网络（TPN），用于为目标视频对象聚合特征表示来生成高质量的视频管建议，这是一种可以高度动态的目标。3）一种策略性地改进的时间匹配网络（TMN +），用于匹配具有更好辨别能力的代表查询管特征，从而实现更高的多样性。我们的TPN和TMN +共同和端到端训练。广泛的实验表明，与基于图像的方法和其他基于视频的扩展相比，我们的方法在两个镜头视频对象检测数据集中产生显着更好的检测结果。代码和数据集将在https://github.com/fanq15/fewx释放。

The tracking-by-detection paradigm today has become the dominant method for multi-object tracking and works by detecting objects in each frame and then performing data association across frames. However, its sequential frame-wise matching property fundamentally suffers from the intermediate interruptions in a video, such as object occlusions, fast camera movements, and abrupt light changes. Moreover, it typically overlooks temporal information beyond the two frames for matching. In this paper, we investigate an alternative by treating object association as clip-wise matching. Our new perspective views a single long video sequence as multiple short clips, and then the tracking is performed both within and between the clips. The benefits of this new approach are two folds. First, our method is robust to tracking error accumulation or propagation, as the video chunking allows bypassing the interrupted frames, and the short clip tracking avoids the conventional error-prone long-term track memory management. Second, the multiple frame information is aggregated during the clip-wise matching, resulting in a more accurate long-range track association than the current frame-wise matching. Given the state-of-the-art tracking-by-detection tracker, QDTrack, we showcase how the tracking performance improves with our new tracking formulation. We evaluate our proposals on two tracking benchmarks, TAO and MOT17 that have complementary characteristics and challenges each other.

Multi-animal tracking (MAT), a multi-object tracking (MOT) problem, is crucial for animal motion and behavior analysis and has many crucial applications such as biology, ecology and animal conservation. Despite its importance, MAT is largely under-explored compared to other MOT problems such as multi-human tracking due to the scarcity of dedicated benchmarks. To address this problem, we introduce AnimalTrack, a dedicated benchmark for multi-animal tracking in the wild. Specifically, AnimalTrack consists of 58 sequences from a diverse selection of 10 common animal categories. On average, each sequence comprises of 33 target objects for tracking. In order to ensure high quality, every frame in AnimalTrack is manually labeled with careful inspection and refinement. To our best knowledge, AnimalTrack is the first benchmark dedicated to multi-animal tracking. In addition, to understand how existing MOT algorithms perform on AnimalTrack and provide baselines for future comparison, we extensively evaluate 14 state-of-the-art representative trackers. The evaluation results demonstrate that, not surprisingly, most of these trackers become degenerated due to the differences between pedestrians and animals in various aspects (e.g., pose, motion, and appearance), and more efforts are desired to improve multi-animal tracking. We hope that AnimalTrack together with evaluation and analysis will foster further progress on multi-animal tracking. The dataset and evaluation as well as our analysis will be made available at https://hengfan2010.github.io/projects/AnimalTrack/.

近年来，视频实例细分（VIS）在很大程度上是通过离线模型提出的，而在线模型由于其性能较低而逐渐吸引了关注。但是，在线方法在处理长期视频序列和正在进行的视频中具有固有的优势，而由于计算资源的限制，离线模型失败了。因此，如果在线模型可以比离线模型获得可比甚至更好的性能，那将是非常可取的。通过解剖当前的在线模型和离线模型，我们证明了性能差距的主要原因是由特征空间中不同实例之间相似外观引起的框架之间存在错误的关联。观察到这一点，我们提出了一个基于对比度学习的在线框架，该框架能够学习更多的歧视实例嵌入，以进行关联，并充分利用历史信息以达到稳定性。尽管它很简单，但我们的方法在三个基准测试上都优于在线和离线方法。具体来说，我们在YouTube-VIS 2019上实现了49.5 AP，比先前的在线和离线艺术分别取得了13.2 AP和2.1 AP的显着改善。此外，我们在OVIS上实现了30.2 AP，这是一个更具挑战性的数据集，具有大量的拥挤和遮挡，超过了14.8 AP的先前艺术。提出的方法在第四次大规模视频对象分割挑战（CVPR2022）的视频实例细分轨道中赢得了第一名。我们希望我们方法的简单性和有效性以及对当前方法的见解，可以阐明VIS模型的探索。

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。

Object recognition techniques using convolutional neural networks (CNN) have achieved great success. However, state-of-the-art object detection methods still perform poorly on large vocabulary and long-tailed datasets, e.g. LVIS.In this work, we analyze this problem from a novel perspective: each positive sample of one category can be seen as a negative sample for other categories, making the tail categories receive more discouraging gradients. Based on it, we propose a simple but effective loss, named equalization loss, to tackle the problem of long-tailed rare categories by simply ignoring those gradients for rare categories. The equalization loss protects the learning of rare categories from being at a disadvantage during the network parameter updating. Thus the model is capable of learning better discriminative features for objects of rare classes. Without any bells and whistles, our method achieves AP gains of 4.1% and 4.8% for the rare and common categories on the challenging LVIS benchmark, compared to the Mask R-CNN baseline. With the utilization of the effective equalization loss, we finally won the 1st place in the LVIS Challenge 2019. Code has been made available at: https: //github.com/tztztztztz/eql.detectron2

Generic Object Tracking (GOT) is the problem of tracking target objects, specified by bounding boxes in the first frame of a video. While the task has received much attention in the last decades, researchers have almost exclusively focused on the single object setting. Multi-object GOT benefits from a wider applicability, rendering it more attractive in real-world applications. We attribute the lack of research interest into this problem to the absence of suitable benchmarks. In this work, we introduce a new large-scale GOT benchmark, LaGOT, containing multiple annotated target objects per sequence. Our benchmark allows researchers to tackle key remaining challenges in GOT, aiming to increase robustness and reduce computation through joint tracking of multiple objects simultaneously. Furthermore, we propose a Transformer-based GOT tracker TaMOS capable of joint processing of multiple objects through shared computation. TaMOs achieves a 4x faster run-time in case of 10 concurrent objects compared to tracking each object independently and outperforms existing single object trackers on our new benchmark. Finally, TaMOs achieves highly competitive results on single-object GOT datasets, setting a new state-of-the-art on TrackingNet with a success rate AUC of 84.4%. Our benchmark, code, and trained models will be made publicly available.

开放世界对象检测（OWOD）是一个具有挑战性的计算机视觉问题，需要检测未知对象并逐渐学习已确定的未知类别。但是，它不能将未知实例区分为多个未知类。在这项工作中，我们提出了一个新颖的OWOD问题，称为未知分类的开放世界对象检测（UC-OWOD）。 UC-OWOD旨在检测未知实例并将其分类为不同的未知类别。此外，我们制定问题并设计一个两阶段的对象检测器来解决UC-OWOD。首先，使用未知的标签意见建议和未知歧视性分类头用于检测已知和未知对象。然后，构建基于相似性的未知分类和未知聚类改进模块，以区分多个未知类别。此外，设计了两个新颖的评估方案，以评估未知类别的检测。丰富的实验和可视化证明了该方法的有效性。代码可在https://github.com/johnwuzh/uc-owod上找到。

To track the 3D locations and trajectories of the other traffic participants at any given time, modern autonomous vehicles are equipped with multiple cameras that cover the vehicle's full surroundings. Yet, camera-based 3D object tracking methods prioritize optimizing the single-camera setup and resort to post-hoc fusion in a multi-camera setup. In this paper, we propose a method for panoramic 3D object tracking, called CC-3DT, that associates and models object trajectories both temporally and across views, and improves the overall tracking consistency. In particular, our method fuses 3D detections from multiple cameras before association, reducing identity switches significantly and improving motion modeling. Our experiments on large-scale driving datasets show that fusion before association leads to a large margin of improvement over post-hoc fusion. We set a new state-of-the-art with 12.6% improvement in average multi-object tracking accuracy (AMOTA) among all camera-based methods on the competitive NuScenes 3D tracking benchmark, outperforming previously published methods by 6.5% in AMOTA with the same 3D detector.

How would you fairly evaluate two multi-object tracking algorithms (i.e. trackers), each one employing a different object detector? Detectors keep improving, thus trackers can make less effort to estimate object states over time. Is it then fair to compare a new tracker employing a new detector with another tracker using an old detector? In this paper, we propose a novel performance measure, named Tracking Effort Measure (TEM), to evaluate trackers that use different detectors. TEM estimates the improvement that the tracker does with respect to its input data (i.e. detections) at frame level (intra-frame complexity) and sequence level (inter-frame complexity). We evaluate TEM over well-known datasets, four trackers and eight detection sets. Results show that, unlike conventional tracking evaluation measures, TEM can quantify the effort done by the tracker with a reduced correlation on the input detections. Its implementation is publicly available online at https://github.com/vpulab/MOT-evaluation.

Panoptic现场了解和跟踪动态代理对于机器人和自动化车辆至关重要，以在城市环境中导航。由于LiDAR提供了方案的精确照明和几何描绘，使用LIDAR点云执行这些任务提供可靠的预测。然而，现有数据集缺乏城市场景类型的多样性，并且具有有限数量的动态对象实例，其阻碍了这些任务的学习以及开发方法的可信基准。在本文中，我们介绍了大规模的Panoptic Nuscenes基准数据集，它扩展了我们流行的NUSCENES DataSet，具有用于语义分割，Panoptic分段和Panoptic跟踪任务的Pock-Wise Trountruth annotations。为了便于比较，我们为我们提出的数据集提供了几个任务的强大基线。此外，我们分析了Panoptic跟踪的现有度量标准的缺点，并提出了一种解决问题的小说实例的Pat度量。我们提供详尽的实验，展示了Panoptic Nuscenes与现有数据集相比的效用，并在Nuscenes.org提供的在线评估服务器。我们认为，此扩展将加快新颖的现场了解动态城市环境的新方法研究。