Passive monitoring of acoustic or radio sources has important applications in modern convenience, public safety, and surveillance. A key task in passive monitoring is multiobject tracking (MOT). This paper presents a Bayesian method for multisensor MOT for challenging tracking problems where the object states are high-dimensional, and the measurements follow a nonlinear model. Our method is developed in the framework of factor graphs and the sum-product algorithm (SPA). The multimodal probability density functions (pdfs) provided by the SPA are effectively represented by a Gaussian mixture model (GMM). To perform the operations of the SPA in high-dimensional spaces, we make use of Particle flow (PFL). Here, particles are migrated towards regions of high likelihood based on the solution of a partial differential equation. This makes it possible to obtain good object detection and tracking performance even in challenging multisensor MOT scenarios with single sensor measurements that have a lower dimension than the object positions. We perform a numerical evaluation in a passive acoustic monitoring scenario where multiple sources are tracked in 3-D from 1-D time-difference-of-arrival (TDOA) measurements provided by pairs of hydrophones. Our numerical results demonstrate favorable detection and estimation accuracy compared to state-of-the-art reference techniques.

Location-aware networks will introduce new services and applications for modern convenience, surveillance, and public safety. In this paper, we consider the problem of cooperative localization in a wireless network where the position of certain anchor nodes can be controlled. We introduce an active planning method that aims at moving the anchors such that the information gain of future measurements is maximized. In the control layer of the proposed method, control inputs are calculated by minimizing the traces of approximate inverse Bayesian Fisher information matrixes (FIMs). The estimation layer computes estimates of the agent states and provides Gaussian representations of marginal posteriors of agent positions to the control layer for approximate Bayesian FIM computations. Based on a cost function that accumulates Bayesian FIM contributions over a sliding window of discrete future timesteps, a receding horizon (RH) control is performed. Approximations that make it possible to solve the resulting tree-search problem efficiently are also discussed. A numerical case study demonstrates the intelligent behavior of a single controlled anchor in a 3-D scenario and the resulting significantly improved localization accuracy.

Source-free domain adaptation aims to adapt a source model trained on fully-labeled source domain data to a target domain with unlabeled target domain data. Source data is assumed inaccessible due to proprietary or privacy reasons. Existing works use the source model to pseudolabel target data, but the pseudolabels are unreliable due to data distribution shift between source and target domain. In this work, we propose to leverage an ImageNet pre-trained feature extractor in a new co-learning framework to improve target pseudolabel quality for finetuning the source model. Benefits of the ImageNet feature extractor include that it is not source-biased and it provides an alternate view of features and classification decisions different from the source model. Such pre-trained feature extractors are also publicly available, which allows us to readily leverage modern network architectures that have strong representation learning ability. After co-learning, we sharpen predictions of non-pseudolabeled samples by entropy minimization. Evaluation on 3 benchmark datasets show that our proposed method can outperform existing source-free domain adaptation methods, as well as unsupervised domain adaptation methods which assume joint access to source and target data.

Motion prediction is highly relevant to the perception of dynamic objects and static map elements in the scenarios of autonomous driving. In this work, we propose PIP, the first end-to-end Transformer-based framework which jointly and interactively performs online mapping, object detection and motion prediction. PIP leverages map queries, agent queries and mode queries to encode the instance-wise information of map elements, agents and motion intentions, respectively. Based on the unified query representation, a differentiable multi-task interaction scheme is proposed to exploit the correlation between perception and prediction. Even without human-annotated HD map or agent's historical tracking trajectory as guidance information, PIP realizes end-to-end multi-agent motion prediction and achieves better performance than tracking-based and HD-map-based methods. PIP provides comprehensive high-level information of the driving scene (vectorized static map and dynamic objects with motion information), and contributes to the downstream planning and control. Code and models will be released for facilitating further research.

In contrast to fully supervised methods using pixel-wise mask labels, box-supervised instance segmentation takes advantage of simple box annotations, which has recently attracted increasing research attention. This paper presents a novel single-shot instance segmentation approach, namely Box2Mask, which integrates the classical level-set evolution model into deep neural network learning to achieve accurate mask prediction with only bounding box supervision. Specifically, both the input image and its deep features are employed to evolve the level-set curves implicitly, and a local consistency module based on a pixel affinity kernel is used to mine the local context and spatial relations. Two types of single-stage frameworks, i.e., CNN-based and transformer-based frameworks, are developed to empower the level-set evolution for box-supervised instance segmentation, and each framework consists of three essential components: instance-aware decoder, box-level matching assignment and level-set evolution. By minimizing the level-set energy function, the mask map of each instance can be iteratively optimized within its bounding box annotation. The experimental results on five challenging testbeds, covering general scenes, remote sensing, medical and scene text images, demonstrate the outstanding performance of our proposed Box2Mask approach for box-supervised instance segmentation. In particular, with the Swin-Transformer large backbone, our Box2Mask obtains 42.4% mask AP on COCO, which is on par with the recently developed fully mask-supervised methods. The code is available at: https://github.com/LiWentomng/boxlevelset.

我们提出MAPTR，这是一个结构化的端到端框架，用于有效的在线矢量化高清图构建。我们提出了一种基于统一的建模方法，即将MAP元素建模为具有一组等效排列的点集，从而避免了地图元素的定义歧义并简化学习。我们采用层次查询嵌入方案来灵活编码结构化的地图信息，并对地图元素学习执行层次结构匹配。 MAPTR在Nuscenes数据集上实现了现有的矢量化MAP构造方法的最佳性能和效率。尤其是，MAPTR-NANO以RTX 3090的实时推理速度（$ 25.1 $ fps）运行，比现有的基于最新的摄像头方法快$ 8 \ times $ $，同时获得$ 3.3 $较高的地图。 Maptr-tiny在更快的速度的同时显着优于现有的最新多模式方法$ 13.5 $地图。定性结果表明，MAPTR在复杂和各种驾驶场景中保持稳定且强大的地图构造质量。可在\ url {https://github.com/hustvl/maptr}上获得丰富的演示，以证明在现实世界情景中的有效性。 MAPTR在自动驾驶中具有巨大的应用价值。代码将发布以促进进一步的研究和应用。

视频中的多目标跟踪需要解决相邻帧中对象之间一对一分配的基本问题。大多数方法通过首先丢弃不可能的对距离大于阈值的不可能对解决问题，然后使用匈牙利算法将对象链接起来以最大程度地减少整体距离。但是，我们发现从重新ID特征计算出的距离的分布可能在不同的视频中有很大差异。因此，没有一个最佳阈值可以使我们安全丢弃不可能的对。为了解决该问题，我们提出了一种有效的方法来实时计算每对对象的边际概率。边际概率可以视为标准化距离，比原始特征距离明显稳定。结果，我们可以为所有视频使用一个阈值。该方法是一般的，可以应用于现有的跟踪器，以在IDF1度量方面获得大约一个点改进。它在MOT17和MOT20基准上取得了竞争成果。此外，计算的概率更容易解释，从而有助于后续后期处理操作。

与使用像素面罩标签的完全监督的方法相反，盒子监督实例细分利用了简单的盒子注释，该盒子注释最近吸引了许多研究注意力。在本文中，我们提出了一种新颖的单弹盒监督实例分割方法，该方法将经典级别设置模型与深度神经网络精致整合在一起。具体而言，我们提出的方法迭代地通过端到端的方式通过基于Chan-Vese的连续能量功能来学习一系列级别集。一个简单的掩码监督的SOLOV2模型可供选择，以预测实例感知的掩码映射为每个实例的级别设置。输入图像及其深度特征都被用作输入数据来发展级别集曲线，其中使用框投影函数来获得初始边界。通过最大程度地减少完全可分化的能量函数，在其相应的边界框注释中迭代优化了每个实例的级别设置。在四个具有挑战性的基准上的实验结果表明，在各种情况下，我们提出的强大实例分割方法的领先表现。该代码可在以下网址获得：https：//github.com/liwentomng/boxlevelset。

在这项工作中，我们为基于视觉的不均衡的BEV表示学习提出了PolarBev。为了适应摄像机成像的预先处理效果，我们将BEV空间横向和辐射上栅格化，并引入极性嵌入分解，以模拟极性网格之间的关联。极性网格被重新排列到类似阵列的常规表示，以进行有效处理。此外，为了确定2到3D对应关系，我们根据假设平面迭代更新BEV表面，并采用基于高度的特征转换。PolarBev在单个2080TI GPU上保持实时推理速度，并且在BEV语义分割和BEV实例分割方面都优于其他方法。展示彻底消融以验证设计。该代码将在\ url {https://github.com/superz-liu/polarbev}上发布。

关于驾驶场景图像的语义细分对于自动驾驶至关重要。尽管在白天图像上已经实现了令人鼓舞的性能，但由于暴露不足和缺乏标记的数据，夜间图像的性能不那么令人满意。为了解决这些问题，我们提出了一个称为双图像自动学习过滤器（拨号过滤器）的附加模块，以改善夜间驾驶条件下的语义分割，旨在利用不同照明下驾驶场景图像的内在特征。拨盘滤波器由两个部分组成，包括图像自适应处理模块（IAPM）和可学习的引导过滤器（LGF）。使用拨号过滤器，我们设计了无监督和有监督的框架，用于夜间驾驶场景细分，可以以端到端的方式进行培训。具体而言，IAPM模块由一个带有一组可区分图像过滤器的小型卷积神经网络组成，可以自适应地增强每个图像，以更好地相对于不同的照明。 LGF用于增强分割网络的输出以获得最终的分割结果。拨号过滤器轻巧有效，可以在白天和夜间图像中轻松应用它们。我们的实验表明，Dail过滤器可以显着改善ACDC_Night和Nightcity数据集的监督细分性能，而它展示了有关无监督的夜间夜间语义细分的最新性能，在黑暗的苏黎世和夜间驾驶测试床上。