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.

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.

3D激光雷达语义细分对于自动驾驶是基础。最近已经提出了几种用于点云数据的无监督域适应性（UDA）方法，以改善不同传感器和环境的模型概括。研究图像域中研究UDA问题的研究人员表明，样品混合可以减轻域的转移。我们提出了一种针对点云UDA的样品混合的新方法，即组成语义混合（Cosmix），这是基于样品混合的第一种UDA方法。 Cosmix由一个两分支对称网络组成，该网络可以同时处理标记的合成数据（源）和现实世界中未标记的点云（目标）。每个分支通过从另一个域中混合选定的数据来在一个域上运行，并使用源标签和目标伪标签的语义信息。我们在两个大规模数据集上评估Cosmix，表明它的表现要优于最先进的方法。我们的代码可在https://github.com/saltoricristiano/cosmix-uda上找到。

3D点云语义细分对于自动驾驶至关重要。文献中的大多数方法都忽略了一个重要方面，即在处理动态场景时如何处理域转移。这可能会极大地阻碍自动驾驶车辆的导航能力。本文推进了该研究领域的最新技术。我们的第一个贡献包括分析点云细分中的新的未开发的方案，即无源的在线无监督域改编（SF-OUDA）。我们在实验上表明，最新的方法具有相当有限的能力，可以使预训练的深网模型以在线方式看不到域。我们的第二个贡献是一种依赖于自适应自我训练和几何传播的方法，以在线调整预训练的源模型，而无需源数据或目标标签。我们的第三个贡献是在一个充满挑战的设置中研究sf-ouda，其中源数据是合成的，目标数据是现实世界中捕获的点云。我们将最近的Synlidar数据集用作合成源，并引入了两个新的合成（源）数据集，这些数据集可以刺激未来的综合自动驾驶研究。我们的实验显示了我们分割方法对数千个现实点云的有效性。代码和合成数据集可在https://github.com/saltoricristiano/gipso-sfouda上找到。

我们介绍了一种简单而有效的方法，可以使用本地3D深度描述符（L3DS）同时定位和映射解决循环闭合检测。 L3DS正在采用深度学习算法从数据从数据中学到的点云提取的斑块的紧凑型表示。通过在通过其估计的相对姿势向循环候选点云登记之后计算对应于相互最近邻接描述符的点之间的度量误差，提出了一种用于循环检测的新颖重叠度量。这种新方法使我们能够在小重叠的情况下精确地检测环并估计六个自由度。我们将基于L3D的循环闭合方法与最近的LIDAR数据的方法进行比较，实现最先进的环路闭合检测精度。此外，我们嵌入了我们在最近的基于边缘的SLAM系统中的循环闭合方法，并对现实世界RGBD-TUM和合成ICL数据集进行了评估。与其原始环路闭合策略相比，我们的方法能够实现更好的本地化准确性。

Recent years have seen a proliferation of research on adversarial machine learning. Numerous papers demonstrate powerful algorithmic attacks against a wide variety of machine learning (ML) models, and numerous other papers propose defenses that can withstand most attacks. However, abundant real-world evidence suggests that actual attackers use simple tactics to subvert ML-driven systems, and as a result security practitioners have not prioritized adversarial ML defenses. Motivated by the apparent gap between researchers and practitioners, this position paper aims to bridge the two domains. We first present three real-world case studies from which we can glean practical insights unknown or neglected in research. Next we analyze all adversarial ML papers recently published in top security conferences, highlighting positive trends and blind spots. Finally, we state positions on precise and cost-driven threat modeling, collaboration between industry and academia, and reproducible research. We believe that our positions, if adopted, will increase the real-world impact of future endeavours in adversarial ML, bringing both researchers and practitioners closer to their shared goal of improving the security of ML systems.

When simulating soft robots, both their morphology and their controllers play important roles in task performance. This paper introduces a new method to co-evolve these two components in the same process. We do that by using the hyperNEAT algorithm to generate two separate neural networks in one pass, one responsible for the design of the robot body structure and the other for the control of the robot. The key difference between our method and most existing approaches is that it does not treat the development of the morphology and the controller as separate processes. Similar to nature, our method derives both the "brain" and the "body" of an agent from a single genome and develops them together. While our approach is more realistic and doesn't require an arbitrary separation of processes during evolution, it also makes the problem more complex because the search space for this single genome becomes larger and any mutation to the genome affects "brain" and the "body" at the same time. Additionally, we present a new speciation function that takes into consideration both the genotypic distance, as is the standard for NEAT, and the similarity between robot bodies. By using this function, agents with very different bodies are more likely to be in different species, this allows robots with different morphologies to have more specialized controllers since they won't crossover with other robots that are too different from them. We evaluate the presented methods on four tasks and observe that even if the search space was larger, having a single genome makes the evolution process converge faster when compared to having separated genomes for body and control. The agents in our population also show morphologies with a high degree of regularity and controllers capable of coordinating the voxels to produce the necessary movements.

Filming sport videos from an aerial view has always been a hard and an expensive task to achieve, especially in sports that require a wide open area for its normal development or the ones that put in danger human safety. Recently, a new solution arose for aerial filming based on the use of Unmanned Aerial Vehicles (UAVs), which is substantially cheaper than traditional aerial filming solutions that require conventional aircrafts like helicopters or complex structures for wide mobility. In this paper, we describe the design process followed for building a customized UAV suitable for sports aerial filming. The process includes the requirements definition, technical sizing and selection of mechanical, hardware and software technologies, as well as the whole integration and operation settings. One of the goals is to develop technologies allowing to build low cost UAVs and to manage them for a wide range of usage scenarios while achieving high levels of flexibility and automation. This work also shows some technical issues found during the development of the UAV as well as the solutions implemented.

We describe a Physics-Informed Neural Network (PINN) that simulates the flow induced by the astronomical tide in a synthetic port channel, with dimensions based on the Santos - S\~ao Vicente - Bertioga Estuarine System. PINN models aim to combine the knowledge of physical systems and data-driven machine learning models. This is done by training a neural network to minimize the residuals of the governing equations in sample points. In this work, our flow is governed by the Navier-Stokes equations with some approximations. There are two main novelties in this paper. First, we design our model to assume that the flow is periodic in time, which is not feasible in conventional simulation methods. Second, we evaluate the benefit of resampling the function evaluation points during training, which has a near zero computational cost and has been verified to improve the final model, especially for small batch sizes. Finally, we discuss some limitations of the approximations used in the Navier-Stokes equations regarding the modeling of turbulence and how it interacts with PINNs.

Reinforcement learning allows machines to learn from their own experience. Nowadays, it is used in safety-critical applications, such as autonomous driving, despite being vulnerable to attacks carefully crafted to either prevent that the reinforcement learning algorithm learns an effective and reliable policy, or to induce the trained agent to make a wrong decision. The literature about the security of reinforcement learning is rapidly growing, and some surveys have been proposed to shed light on this field. However, their categorizations are insufficient for choosing an appropriate defense given the kind of system at hand. In our survey, we do not only overcome this limitation by considering a different perspective, but we also discuss the applicability of state-of-the-art attacks and defenses when reinforcement learning algorithms are used in the context of autonomous driving.