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IEEE Transactions on Radar Systems 2025年 3卷 789-798页

作者： Hägele, Stefan Seguel, Fabian Kahya, Sabri Mustafa Steinbach, Eckehard School of Computation Information and Technology Department of Computer Engineering Chair of Media Technology Munich Institute of Robotics and Machine Intelligence (MIRMI) Technical University of Munich Munich Germany

The ability of mmWave radar to penetrate lightweight materials and provide non-visual insights into obscured areas represents a significant advantage over camera or LiDAR sensors. This capability enables mmWave radar to detect humans behind thin walls or identify occluded objects stored within luggage or packages. The latter capability is particularly valuable in industrial, logistics, and manufacturing applications, where the ability to "look inside the box without opening it" can greatly enhance efficiency and security. However, the current state-of-the-art in these applications relies on expensive custom-built large antenna array imaging scanners, coupled with image-based object detection algorithms, to detect and classify occluded or concealed objects. To address this challenge more efficiently, we propose a lightweight classification approach for detecting various occluded objects inside a cardboard box. We employ a standard off-the-shelf mmWave 4D FMCW imaging radar. This is combined with a deep learning-based classification method in form of a dual-stream CNN approach to process complex IQ radar signals. This approach reaches in our experiments an overall accuracy of 95.15% on average over a collection of ten different concealed objects. © 2023 IEEE.

关键词： Frequency modulation

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Care3D: An Active 3D Object Detection Dataset of Real Robotic-Care Environments

Care3D: An Active 3D Object Detection Dataset of Real Roboti...

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2023 IEEE International Symposium on Multimedia, ISM 2023

作者： Adam, Michael G. Eger, Sebastian Piccolrovazzi, Martin Iskandar, Maged Vogel, Joern Dietrich, Alexander Bien, Seongjien Skerlj, Jon Naceri, Abdeldjallil Steinbach, Eckehard Albu-Schaeffer, Alin Haddadin, Sami Burgard, Wolfram Technical University of Munich Media Technology Department of Computer Engineering School of Computation Information and Technology Munich Germany Wessling Germany Robotics and Systems Intelligence Department of Computer Engineering School of Computation Information and Technology Technical University of Munich Munich Germany University of Technology Nuremberg Engineering Department Nuremberg Germany

ISBN: (纸本)9798350395761

As labor shortage increases in the health sector, the demand for assistive robotics grows. However, the needed test data to develop those robots is scarce, especially for the application of active 3D object detection, where no real data exists at all. This short paper counters this by introducing such an annotated dataset of real environments. The captured environments represent areas which are already in use in the field of robotic health care research. We further provide ground truth data within one room, for assessing SLAM algorithms running directly on a health care robot. © 2023 IEEE.

关键词： Object detection

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Towards AI-assisted digital twins for smart railways: preliminary guideline and reference architecture

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Journal of Reliable Intelligent Environments 2023年第3期9卷 303-317页

作者： De Donato, Lorenzo Dirnfeld, Ruth Somma, Alessandra De Benedictis, Alessandra Flammini, Francesco Marrone, Stefano Saman Azari, Mehdi Vittorini, Valeria Department of Electrical Engineering and Information Technology University of Naples Federico II Via Claudio 21 Naples80125 Italy Department of Computer Science and Media Technology Linnaeus University Växjö Sweden School of Innovation Design and Engineering Mälardalen University Eskilstuna Sweden

In the last years, there has been a growing interest in the emerging concept of digital twins (DTs) among software engineers and researchers. DTs not only represent a promising paradigm to improve product quality and optimize production processes, but they also may help enhance the predictability and resilience of cyber-physical systems operating in critical contexts. In this work, we investigate the adoption of DTs in the railway sector, focusing in particular on the role of artificial intelligence (AI) technologies as key enablers for building added-value services and applications related to smart decision-making. In this paper, in particular, we address predictive maintenance which represents one of the most promising services benefiting from the combination of DT and AI. To cope with the lack of mature DT development methodologies and standardized frameworks, we detail a workflow for DT design and development specifically tailored to a predictive maintenance scenario and propose a high-level architecture for AI-enabled DTs supporting such workflow. © 2023, The Author(s).

关键词： Cyber Physical System

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Light bottle transformer based large scale point cloud classification

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Optoelectronics Letters 2023年第6期19卷 377-384页

作者： XIE En ZHANG Zhiyong ZHANG Guodao CHEN Pingkuo GE Yisu College of Computer Information and Engineering Nanchang Institute of TechnologyNanchang 330044China School of Information Engineering East China University of TechnologyNanchang 330000China Department of Digital Media Technology Hangzhou Dianzi UniversityHangzhou 310023China Great Bay University Dongguan 523000China College of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou 325100China

With the rapid development of computer vision,point clouds technique was widely used in practical applications,such as obstacle detection,roadside detection,smart city construction,***,how to efficiently identify the large scale point clouds is still an open *** relieving the large computation consumption and low accuracy problem in point cloud classification,a large scale point cloud classification framework based on light bottle transformer(light-Bot Net)is ***,the two-dimensional(2D)and three-dimensional(3D)feature values of large scale point cloud were extracted for constructing point cloud feature images,which employed the prior knowledge to normalize the point cloud ***,the feature images are input to the classification network,and the light-Bot Net network is applied for point cloud *** is an interesting attempt to combine the traditional image features with the transformer *** proving the performance of the proposed method,the large scale point cloud benchmark Oakland 3D is *** the experiments,the proposed method achieved 98.1%accuracy on the Oakland 3D *** with the other methods,it can both reduce the memory consumption and improve the classification accuracy in large scale point cloud classification.

关键词： computer cloud point

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BPCoach: Exploring Hero Drafting in Professional MOBA Tournaments via Visual Analytics

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Proceedings of the ACM on Human-computer Interaction 2024年第CSCW1期8卷 1-31页

作者： Liu, Shiyi Ma, Ruofei Zhao, Chuyi Li, Zhenbang Xiao, Jianpeng Li, Quan School of Information Science and Technology ShanghaiTech University Shanghai China Department of Computer Science Tufts University MedfordMA United States Guangzhou Qucheng Culture Media Co. Guangzhou China School of Information Science and Technology ShanghaiTech University Shanghai Engineering Research Center of Intelligent Vision and Imaging Shanghai China

Hero drafting for multiplayer online arena (MOBA) games is crucial because drafting directly affects the outcome of a match. Both sides take turns to "ban"/"pick" a hero from a roster of approximately 100 heroes to assemble their drafting. In professional tournaments, the process becomes more complex as teams are not allowed to pick heroes used in the previous rounds with the "best-of-N" rule. Additionally, human factors including the team's familiarity with drafting and play styles are overlooked by previous studies. Meanwhile, the huge impact of patch iteration on drafting strengths in the professional tournament is of concern. To this end, we propose a visual analytics system, BPCoach, to facilitate hero drafting planning by comparing various drafting through recommendations and predictions and distilling relevant human and in-game factors. Two case studies, expert feedback, and a user study suggest that BPCoach helps determine hero drafting in a rounded and efficient manner. © 2023 Copyright held by the owner/author(s).

关键词： Visualization

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Dynamic weight-based connectivity recovery in wireless sensor and actor networks

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Journal of Supercomputing 2024年第1期80卷 734-760页

作者： Chiang, Mao-Lun Hsieh, Hui-Ching Lin, Tzu-Ling Chang, Tsui-Ping Chen, Hong-Wei Bachelor Degree Program of Artificial Intelligence National Taichung University of Science and Technology Taichung City Taiwan Department of New Media Communication Hsing Wu University New Taipei City Taiwan Department of Business Management National Taichung University of Science and Technology Taichung City Taiwan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung City Taiwan Department of Information and Communication Engineering Chaoyang University of Technology Taichung City Taiwan

Wireless sensors and actor networks (WSANs) have been widely used in various fields, from basic data collection to precise real-time control and monitoring, including battlefield monitoring, rescue, and exploration. The actor nodes in a WSAN play a key role in processing and transmitting data, and because of the harsh environmental conditions encountered, network node failures are prone to occur. The failure of an actor causes a loss of connectivity with its neighbor nodes, resulting in network splits and failure to deliver messages between nodes. Therefore, determining how to quickly and autonomously recover network connectivity while minimizing the moving distance of backup nodes and the number of relocated nodes is crucial. In this paper, we propose a dynamic weight-based connectivity recovery (DWCR) method to minimize the total moving distance of actor nodes, the number of relocated nodes, the total messages sent, and the coverage loss rate. The proposed DWCR method involves selecting a backup node for each critical node by dynamically adjusting the weight. If a critical node fails, the backup node starts to move to restore the connectivity of the failed node. The simulation results indicate that the proposed DWCR method outperforms the recovery through inward motion (RIM) method and other methods in terms of minimizing the total moving distance, the number of relocated nodes, and the total messages sent. However, because the RIM algorithm requires the relocation of more nodes during the network process, the DWCR algorithm is slightly inferior to the RIM algorithm in terms of coverage loss rate. Nevertheless, the DWCR algorithm still outperforms the distributed actor recovery algorithm and the distributed autonomous connectivity restoration method based on finite state machine algorithm. © 2023, The Author(s), under exclusive licence to Springer Science+Business media, LLC, part of Springer Nature.

关键词： Recovery

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DIG: Improved DINO for Graffiti Detection

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3557-3569页

作者： Wang, Bingshu Mao, Qianchen Liu, Aifei Chen, Long Chen, C. L. Philip Northwestern Polytechnical University School of Software Xi’an710129 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen518060 China University of Macao Department of Computer and Information Science China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China

Graffiti detection is essential in historic building protection and urban neighborhood management. Graffiti detection has made significant progress in recent years based on the development of deep learning. However, small-scale graffiti, interference from the background, and the false detection of word parts in graffiti make it a challenging problem. This article proposes a Transformer-based high-precision graffiti detection method, namely DIG. Precisely, it consists of three modules: 1) Spatial query selection (SQS), scale-aware IoU loss (SIL);2) Denoising Task with binary contrastive denoising (BCDN);and 3) IoU-guided box denoising (IBD) modules. To detect small-scale graffiti, this paper proposes SIL to help the loss function to perceive small-scale graffiti and large-scale graffiti fairly. To reduce the false detection of word parts, this article presents the SQS module, which integrates spatial information into the query selection process of the Encoder to filter out falsely detected bounding boxes within the graffiti. To reduce the interference from the background, this article introduces a denoising task with BCDN and IBD modules, improving the model’s ability to distinguish graffiti from the background and accurately select appropriate bounding boxes. A large number of experimental results on the STORM dataset show that our method achieves state-of-the-art results with an AP50 of 87.9%. Moreover, DIG achieved competitive results on the FineFM dataset for mask detection. This indicates that DIG can also be conveniently transferred to detect other scenarios. © 2013 IEEE.

关键词： Historic preservation

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Fuzzy-NMS: Improving 3D Object Detection With Fuzzy Classification in NMS

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Wang, Li Zhang, Xinyu Zhao, Fachuan Wu, Chuze Wang, Yichen Song, Ziying Yang, Lei Xu, Bin Li, Jun Ge, Shuzhi Sam School of Machanical Engineering Beijing Institute of Technology Beijing China School of Vehicle and Mobility Tsinghua University Beijing China School of Computer and Information Technology Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering Interactive Digital Media Institute Social Robotics Laboratory National University of Singapore Singapore

Non-maximum suppression (NMS) is an essential post-processing module in many 3D object detection frameworks to remove overlapping candidate bounding boxes. However, an overreliance on classification scores and difficulties in determining appropriate thresholds can affect the resulting accuracy directly. To address these issues, we introduce fuzzy learning into NMS and propose a novel generalized Fuzzy-NMS module to achieve finer candidate bounding box filtering. The proposed Fuzzy-NMS module combines the volume and clustering density of candidate bounding boxes, refining them with a fuzzy classification method and optimizing the appropriate suppression thresholds to reduce uncertainty in the NMS process. Adequate validation experiments use the mainstream KITTI and large-scale Waymo 3D object detection benchmarks. The results of these tests demonstrate the proposed Fuzzy-NMS module can improve the accuracy of numerous recently NMS-based detectors significantly, including PointPillars, PV-RCNN, and IA-SSD, etc. This effect is particularly evident for small objects such as pedestrians and bicycles. As a plug-and-play module, Fuzzy-NMS does not need to be retrained and produces no obvious increases in inference time. IEEE

关键词： Object detection

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Multi-View Graph Embedding Learning for Image Co-Segmentation and Co-Localization

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IEEE Transactions on Circuits and Systems for Video technology 2024年第6期34卷 4942-4956页

作者： Huang, Aiping Li, Lijian Zhang, Le Niu, Yuzhen Zhao, Tiesong Lin, Chia-Wen Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou350108 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou350108 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering The Fujian Science and Technology Innovation Laboratory for Optoelectronic Information Fuzhou350108 China Institute of Communications Engineering National Tsing Hua University Department of Electrical Engineering Hsinchu30013 Taiwan

Image co-segmentation and co-localization exploit inter-image information to identify and extract foreground objects with a batch mode. However, they remain challenging when confronted with large object variations or complex backgrounds. This paper proposes a multi-view graph embedding (MV-Gem) learning scheme which integrates diversity, robustness and discernibility of object features to alleviate this phenomenon. To encourage the diversity, the deep co-information containing both low-layer general representations and high-layer semantic information is generated to form a multi-view feature pool for comprehensive co-object description. To enhance the robustness, a multi-view adaptive weighted learning is formulated to fuse the deep co-information for feature complementation. To ensure the discernibility, the graph embedding and sparse constraint are embedded into the fusion formulation for feature selection. The former aims to inherit important structures from multiple views, and the latter further selects important features to restrain irrelevant backgrounds. With these techniques, MV-Gem gradually recovers all co-objects through optimization iterations. Extensive experimental results on real-world datasets demonstrate that MV-Gem is capable of locating and delineating co-objects in an image group. © 1991-2012 IEEE.

关键词： Semantics

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Identification and Classification of Crowd Activities

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computers, Materials & Continua 2022年第7期72卷 815-832页

作者： Manar Elshahawy Ahmed O.Aseeri Shaker El-Sappagh Hassan Soliman Mohammed Elmogy Mervat Abu-Elkheir Department of Information Technology Faculty of Computers and InformationMansoura UniversityEgypt Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Information Systems Faculty of Computers and Artificial IntelligenceBenha UniversityEgypt Faculty of Computer Science and Engineering Galala University435611SuezEgypt Department of Computer Science Faculty of Media Engineering and TechnologyGerman UniversityEgypt

The identification and classification of collective people’s activities are gaining momentum as significant themes in machine learning,with many potential applications *** need for representation of collective human behavior is especially crucial in applications such as assessing security conditions and preventing crowd *** paper investigates the capability of deep neural network(DNN)algorithms to achieve our carefully engineered pipeline for crowd *** includes three principal stages that cover crowd analysis ***,individual’s detection is represented using the You Only Look Once(YOLO)model for human detection and Kalman filter for multiple human tracking;Second,the density map and crowd counting of a certain location are generated using bounding boxes from a human detector;and Finally,in order to classify normal or abnormal crowds,individual activities are identified with pose *** proposed system successfully achieves designing an effective collective representation of the crowd given the individuals in addition to introducing a significant change of crowd in terms of activities *** results onMOT20 and SDHA datasets demonstrate that the proposed system is robust and *** framework achieves an improved performance of recognition and detection peoplewith a mean average precision of 99.0%,a real-time speed of 0.6ms non-maximumsuppression(NMS)per image for the SDHAdataset,and 95.3%mean average precision for MOT20 with 1.5ms NMS per image.

关键词： Crowd analysis individual detection You Only Look Once(YOLO) multiple object tracking kalman filter pose estimation

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