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IEEE Winter Applications and computer vision Workshops (WACVW)

作者： Mickael Cormier Andreas Specker Julio C. S. Jacques Lennart Moritz Jürgen Metzler Thomas B. Moeslund Kamal Nasrollahi Sergio Escalera Jürgen Beyerer Fraunhofer IOSB Germany Karlsruhe Institute of Technology Germany Fraunhofer Center for Machine Learning Germany University of Barcelona Spain Computer Vision Center Spain Aalborg University Denmark Milestone Systems Denmark

Attribute-based person retrieval enables individuals to be searched and retrieved using their soft biometric features, for instance, gender, accessories, and clothing colors. The process has numerous practical use cases, such as surveillance, retail, or smart cities. Notably, attribute-based person retrieval empowers law enforcement agencies to efficiently comb through vast volumes of surveillance footage from extensive multi-camera networks, facilitating the swift localization of missing persons or criminals. However, for real-world application, attribute-based person retrieval is required to generalize to multiple settings in indoor and outdoor scenarios with their respective challenges. For its second edition, the WACV 2024 Pedestrian Attribute Recognition and Attribute-based Person Retrieval Challenge (UPAR-Challenge) aimed once again to spotlight the current challenges and limitations of existing methods to bridge the domain gaps in real-world surveillance contexts. Analogous to the first edition, two tracks are offered: pedestrian attribute recognition and attribute-based person retrieval. The UPAR-Challenge 2024 dataset extends the UPAR dataset with the introduction of harmonized annotations for the MEVID dataset, which is used as a novel test domain. To this aim, 1.1M additional annotations were manually labeled and validated. Each track evaluates the robustness of the competing methods to domain shifts by training and evaluating on data from entirely different domains. The challenge attracted 82 registered participants, which was considered a success from the organiz ers' perspective. While ten competing teams surpassed the baseline for track 1, no team managed to outperform the baseline on track 2, emphasizing the task's difficulty. This work describes the challenge design, the adopted dataset, obtained results, as well as future directions on the topic. The UPAR-Challenge dataset is available on GitHub: https:/***/speckean/upar_challenge.

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Blessemflood21: Advancing Flood Analysis with a High-Resolution Georeferenced Dataset for Humanitarian Aid Support

Blessemflood21: Advancing Flood Analysis with a High-Resolut...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Vladyslav Polushko Alexander Jenal Jens Bongartz Immanuel Weber Damjan Hatic Ronald Rösch Thomas März Markus Rauhut Andreas Weinmann Image Processing Department Fraunhofer ITWM Kaiserslautern Germany Working Group Algorithms for Computer Vision Imaging and Data Analysis Darmstadt Germany Center for Machine Learning and Sensor Technology Hochschule Koblenz Remagen Germany

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Floods are an increasingly common global threat, causing emergencies and severe damage to infrastructure. During crises, organisations such as the World Food Programme use remotely sensed imagery, typically obtained through drones, for rapid situational analysis to plan life-saving actions. computer vision tools are needed to support task force experts on-site in the evaluation of the imagery to improve their efficiency and to allocate resources strategically. We introduce the BlessemFlood21 dataset to stimulate research on efficient flood detection tools. The imagery was acquired during the 2021 Erftstadt-Blessem flooding event and consists of high-resolution and georeferenced RGB-NIR images. In the resulting RGB dataset, the images are supplemented with detailed water masks, obtained via a semi-supervised human-in-the-loop technique, where in particular the NIR information is leveraged to classify pixels as either water or non-water. We evaluate our dataset by training and testing established Deep learning models for semantic segmentation. With BlessemFlood21 we provide labeled high-resolution RGB data and a baseline for further development of algorithmic solutions tailored to flood detection in RGB imagery.

关键词： Deep learning Training Semantic segmentation Human in the loop Sensors Floods Task analysis

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Transforming Combinatorial Optimization Problems in Fourier Space: Consequences and Uses

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IEEE Transactions on Evolutionary Computation 2024年

作者： Elorza, Anne Benavides, Xabier Ceberio, Josu Hernando, Leticia Lozano, Jose A. University of the Basque Country UPV/EHU Intelligent Systems Group Department of Mathematics Leioa48940 Spain University of the Basque Country UPV/EHU Intelligent Systems Group Department of Computer Science and Artificial Intelligence Donostia-San Sebastian20018 Spain Basque Center for Applied Mathematics Machine Learning Group Bilbao48009 Spain

We analyze three permutation-based combinatorial optimization problems in Fourier space, namely, the Quadratic Assignment Problem, the Linear Ordering Problem and the symmetric and non-symmetric Traveling Salesperson Problem. In previous studies, one can find a number of theorems with necessary conditions that the Fourier coefficients of the aforementioned problems must satisfy. In this manuscript, we prove the sufficiency of these conditions, which implies that they constitute the exact characterization of the problems in Fourier space. In addition, the Fourier coefficients of the Linear Ordering Problem and the symmetric and non-symmetric Traveling Salesperson Problem are completely characterized by showing certain proportionality patterns that they must follow. Taking the characterization in Fourier space of the problems as a basis, we study classes of equivalent instances of the Linear Ordering Problem and the symmetric and non-symmetric Traveling Salesperson Problem, considering that two instances are equivalent if they have the same objective function. Furthermore, we give canonical representations for each problem in such a way that the input matrices have the minimum number of non-zero parameters. © 2024 IEEE.

关键词： Combinatorial optimization

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Dexterous Pre-grasp Manipulation for Human-like Functional Categorical Grasping: Deep Reinforcement learning and Grasp Representations

arXiv

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arXiv 2023年

作者： Pavlichenko, Dmytro Behnke, Sven Group Computer Science Institute VI – Intelligent Systems and Robotics The Center for Robotics The Lamarr Institute for Machine Learning and Artificial Intelligence University of Bonn Germany

Many objects, such as tools and household items, can be used only if grasped in a very specific way—grasped functionally. Often, a direct functional grasp is not possible, though. We propose a method for learning a dexterous pre-grasp manipulation policy to achieve human-like functional grasps using deep reinforcement learning. We introduce a dense multi-component reward function that enables learning a single policy, capable of dexterous pre-grasp manipulation of novel instances of several known object categories with an anthropomorphic hand. The policy is learned purely by means of reinforcement learning from scratch, without any expert demonstrations. It implicitly learns to reposition and reorient objects of complex shapes to achieve given functional grasps. In addition, we explore two different ways to represent a desired grasp: explicit and more abstract, constraint-based. We show that our method consistently learns to successfully manipulate and achieve desired grasps on previously unseen object instances of known categories using both grasp representations. Training is completed on a single GPU in under three hours. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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SPEAR: Design and Implementation of an Advanced Virtual Assistant 4

SPEAR: Design and Implementation of an Advanced Virtual Assi...

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4th International Conference on Sustainable Expert systems, ICSES 2024

作者： Jain, Garima Shukla, Amita Bairwa, Nitesh Kumar Chaudhary, Anamika Patel, Ashish Jain, Ankush Noida Institute of Engineering and Technology Department of Computer Science and Buisness Systems Greater Noida India Noida Institute of Engineering and Technology Department of Artificial Intelligence Greater Noida India Noida Institute of Engineering and Technology Department of Data Science Greater Noida India Dronacharya Group of Institutions Department of Artificial Intelligence and Machine Learning Greater Noida India Netaji Subhash University of Technology Department of Computer Science and Enginnering Delhi India

ISBN: (纸本)9798331540364

This research presents the development and evaluation of SPEAR, an advanced voice-activated personal desktop assistant designed to address challenges in existing virtual assistant technology, such as limited language support, low recognition accuracy, and inadequate task automation. Despite recent advancements in Natural Language Processing (NLP) and machine learning, implementation in current systems remains subpar. SPEAR achieves an 88% overall command execution success rate, with a speech recognition accuracy of 92% and an average response time of 1.8 seconds, demonstrating efficiency in real-time interactions. Developed using Python and machine learning libraries, SPEAR supports 15 voice commands across multiple platforms, excelling in note creation (95% success), web searching (94%), and information retrieval (92%). Its natural language understanding score of 0.86 and context retention rate of 75% highlight its conversational capabilities. While the assistant performs well in messaging functionality (85% success) and currently supports three languages, expanding its multilingual capabilities is essential for broader user engagement. Metrics indicate a satisfaction score of 8.2/10 and an average of 12 daily interactions per user, showcasing its practical utility. Additionally, the system's text-to-speech quality (8.5/10) and error recovery rate (82 %) affirm its robustness. These findings contribute valuable insights for future research, focusing on enhancing multilingual support, broadening task categories, and improving context retention, ultimately positioning SPEAR to rival established industry leaders by offering a comprehensive suite of services. © 2024 IEEE.

关键词： Speech recognition

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LiDAR-Based Registration Against Georeferenced Models for Globally Consistent Allocentric Maps

LiDAR-Based Registration Against Georeferenced Models for Gl...

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IEEE International Workshop on Safety, Security, and Rescue Robotics (SSRR)

作者： Jan Quenzel Linus T. Mallwitz Benedikt T. Arnold Sven Behnke Autonomous Intelligent Systems Group Computer Science Institute VI - Intelligent Systems and Robotics Center for Robotics and Lamarr Institute for Machine Learning and Artificial Intelligence University of Bonn Germany Fraunhofer FIT Germany Fraunhofer IAIS Germany

ISBN: (数字)9798331510954

ISBN: (纸本)9798331510961

Modern unmanned aerial vehicles (UAVs) are irreplaceable in search and rescue (SAR) missions to obtain a situational overview or provide closeups without endangering personnel. However, UAVs heavily rely on global navigation satellite system (GNSS) for localization which works well in open spaces, but the precision drastically degrades in the vicinity of buildings. These inaccuracies hinder aggregation of diverse data from multiple sources in a unified georeferenced frame for SAR operators. In contrast, CityGML models provide approximate building shapes with accurate georeferenced poses. Besides, LiDAR works best in the vicinity of 3D structures. Hence, we refine coarse GNSS measurements by registering LiDAR maps against CityGML and digital elevation map (DEM) models as a prior for allocentric mapping. An intuitive plausibility score selects the best hypothesis based on occupancy using a 2D height map. Afterwards, we integrate the registration results in a continuous-time spline-based pose graph optimizer with LiDAR odometry and further sensing modalities to obtain globally consistent, georeferenced trajectories and maps. We evaluate the viability of our approach on multiple flights captured at two distinct testing sites. Our method successfully reduced GNSS offset errors from up-to 16 m to below 0.5 m on multiple flights. Furthermore, we obtain globally consistent maps w.r.t. prior 3D geospatial models.

关键词： Global navigation satellite system Solid modeling Laser radar Three-dimensional displays Buildings Data models Trajectory Geospatial analysis Splines (mathematics) Testing

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BLESSEMFLOOD21: ADVANCING FLOOD ANALYSIS WITH A HIGH-RESOLUTION GEOREFERENCED DATASET FOR HUMANITARIAN AID SUPPORT

arXiv

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arXiv 2024年

作者： Polushko, Vladyslav Jenal, Alexander Bongartz, Jens Weber, Immanuel Hatic, Damjan Rösch, Ronald März, Thomas Rauhut, Markus Weinmann, Andreas Image Processing Department Fraunhofer ITWM Kaiserslautern Germany Working Group Algorithms for Computer Vision Imaging and Data Analysis Hochschule Darmstadt Darmstadt Germany Center for Machine Learning and Sensor Technology Hochschule Koblenz Remagen Germany

关键词： Floods

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Sit Back and Relax: learning to Drive Incrementally in All Weather Conditions

Sit Back and Relax: Learning to Drive Incrementally in All W...

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IEEE Symposium on Intelligent Vehicle

作者： Stefan Leitner M. Jehanzeb Mirza Wei Lin Jakub Micorek Marc Masana Mateusz Kozinski Horst Possegger Horst Bischof Institute for Computer Graphics and Vision Graz University of Technology Austria Christian Doppler Laboratory for Embedded Machine Learning Christian Doppler Laboratory for Semantic 3D Computer Vision Silicon Austria Labs TU Graz - SAL Dependable Embedded Systems Lab

In autonomous driving scenarios, current object detection models show strong performance when tested in clear weather. However, their performance deteriorates significantly when tested in degrading weather conditions. In addition, even when adapted to perform robustly in a sequence of different weather conditions, they are often unable to perform well in all of them and suffer from catastrophic forgetting. To efficiently mitigate forgetting, we propose Domain-Incremental learning through Activation Matching (DILAM), which employs unsupervised feature alignment to adapt only the affine parameters of a clear weather pre-trained network to different weather conditions. We propose to store these affine parameters as a memory bank for each weather condition and plug-in their weather-specific parameters during driving (i.e. test time) when the respective weather conditions are encountered. Our memory bank is extremely lightweight, since affine parameters account for less than 2% of a typical object detector. Furthermore, contrary to previous domain-incremental learning approaches, we do not require the weather label when testing and propose to automatically infer the weather condition by a majority voting linear classifier.

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LiDAR-based Registration against Georeferenced Models for Globally Consistent Allocentric Maps

arXiv

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arXiv 2024年

作者： Quenzel, Jan Mallwitz, Linus T. Arnold, Benedikt T. Behnke, Sven Autonomous Intelligent Systems Group Computer Science Institute VI – Intelligent Systems and Robotics Germany Center for Robotics Lamarr Institute for Machine Learning and Artificial Intelligence University of Bonn Germany Fraunhofer FIT Germany Fraunhofer IAIS Germany

关键词： Continuous time systems

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learning-based Relational Object Matching Across Views

Learning-based Relational Object Matching Across Views

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Cathrin Elich Iro Armeni Martin R. Oswald Marc Pollefeys Joerg Stueckler Embodied Vision Group Max Planck Institute for Intelligent Systems Tuebingen Germany Max Planck ETH Center for Learning Systems Computer Vision and Geometry Lab ETH Zurich Switzerland University of Amsterdam Netherlands Microsoft Mixed Reality and AI Lab Zurich Switzerland

Intelligent robots require object-level scene understanding to reason about possible tasks and interactions with the environment. Moreover, many perception tasks such as scene reconstruction, image retrieval, or place recognition can benefit from reasoning on the level of objects. While keypoint-based matching can yield strong results for finding correspondences for images with small to medium view point changes, for large view point changes, matching semantically on the object-level becomes advantageous. In this paper, we propose a learning-based approach which combines local keypoints with novel object-level features for matching object detections between RGB images. We train our object-level matching features based on appearance and inter-frame and cross-frame spatial relations between objects in an associative graph neural network. We demonstrate our approach in a large variety of views on realistically rendered synthetic images. Our approach compares favorably to previous state-of-the-art object-level matching approaches and achieves improved performance over a pure keypoint-based approach for large view-point changes.

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