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Rlm-tracking: online multi-pedestrian tracking supported by relative location mapping

INTERNATIONAL JOURNAL OF machine LEARNING AND CYBERNETICS 2024年第7期15卷 2881-2897页

作者： Ren, Kai Hu, Chuanping xi, Hao Univ Zhengzhou Sch Elect & Informat Engn Zhengzhou Henan Peoples R China

The challenge of multi-object tracking stands as a fundamental focus in computer vision research, finding widespread applications in areas such as public safety, transportation, autonomous vehicles, robotics, and other domains involving artificial intelligence. Given the intricate nature of natural scenes, the occurrence of object occlusion and semi-occlusion is commonplace in basic tracking tasks. These factors often result in challenges such as ID switching, object loss, detection errors, and misaligned bounding boxes, thereby significantly impacting the precision of multi-object *** paper aims to address the aforementioned issues and proposes a novel multi-object tracker, incorporating Relative location mapping (RLM) and Target region density (TRD) modeling. The new tracker is more sensitive to differences in the spatial relationships between targets, allowing it to dynamically introduce low-scoring detection boxes into different regions based on the density of target regions in the image. This improves the accuracy of target tracking while avoiding the consumption of a significant amount of computational *** research results indicate that when applying this method to state-of-the-art multi-object tracking approaches, the proposed model achieves improvements of 0.4 to 0.8 points in the HOTA and IDF1 metrics on the MOT17 and MOT20 datasets. This demonstrates the effectiveness of the proposed method in enhancing multi-object tracking performance.

关键词： Relative location mapping Multi-target tracking Video processing Kalman filtering artificial intelligence

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artificial intelligence in advanced surgical training An overview

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UNFALLCHIRURGIE 2025年 1-7页

作者： Deichsel, Adrian Oettl, Felix C. Pawelczyk, Johannes Usov, Lea Haffer, Henryk Univ Klinikum Munster Klin Unfall Hand & Wiederherstellungschirurg Albert Schweitzer Campus 1 D-48149 Munster Germany Balgrist Univ Klin Zurich Switzerland Hosp Special Surg New York NY USA Tech Univ Munich Klinikum Rechts Isar Sekt Sportorthopadie Munich Germany Univ Klinikum Frankfurt Main Abt Unfallchirurg & Orthopadie Frankfurt Germany Charite Univ Med Berlin Ctr Muskuloskeletale Chirurg Berlin Germany

The integration of artificial intelligence (AI) into medical education and advanced training is gaining in importance. Especially in advanced surgical training AI offers potential to support both theoretical and practical learning processes. applications include interactive chatbots, digital assistance systems and automated assessment of surgical skills using computer vision and machine learning. This article provides an overview of current developments, opportunities and challenges of AI in advanced surgical training.

关键词： Assessment Feedback Training Digital assistant Ethics Digital assistant Ethics

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Computer vision Techniques in Manufacturing

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2023年第1期53卷 105-117页

作者： Zhou, Longfei Zhang, Lin Konz, Nicholas MIT Comp Sci & Artificial Intelligence Lab 77 Massachusetts Ave Cambridge MA 02139 USA Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Duke Univ Dept Elect & Comp Engn Durham NC 27708 USA

Computer vision (CV) techniques have played an important role in promoting the informatization, digitization, and intelligence of industrial manufacturing systems. Considering the rapid development of CV techniques, we present a comprehensive review of the state of the art of these techniques and their applications in manufacturing industries. We survey the most common methods, including feature detection, recognition, segmentation, and three-dimensional modeling. A system framework of CV in the manufacturing environment is proposed, consisting of a lighting module, a manufacturing system, a sensing module, CV algorithms, a decision-making module, and an actuator. applications of CV to different stages of the entire product life cycle are then explored, including product design, modeling and simulation, planning and scheduling, the production process, inspection and quality control, assembly, transportation, and disassembly. Challenges include algorithm implementation, data preprocessing, data labeling, and benchmarks. Future directions include building benchmarks, developing methods for nonannotated data processing, developing effective data preprocessing mechanisms, customizing CV models, and opportunities aroused by 5G.

关键词： Image edge detection Image segmentation Task analysis Robot sensing systems Sensors Feature detection Three-dimensional displays Assembly computer vision (CV) deep learning inspection machine intelligence machine learning manufacturing production robotics survey

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Interpretation and Use of Applied/Operational machine Learning and artificial intelligence in Surgery

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SURGICAL CLINICS OF NORTH AMERICA 2023年第2期103卷 317-333页

作者： Douglas, Molly J. Callcut, Rachel Celi, Leo Anthony Merchant, Nirav Univ Arizona Dept Surg 1501 N Campbell Ave Tucson AZ 85724 USA Univ Calif Davis Trauma Acute Care Surg & Surg Crit Care 2335 Stockton Blvd Sacramento CA 95817 USA MIT Lab Computat Physiol 77 Massachusetts Ave Cambridge MA 02139 USA Beth Israel Deaconess Med Ctr Boston MA USA Univ Arizona Data Sci Inst 1230 North Cherry Ave Tucson AZ 85721 USA

applications for artificial intelligence (AI) and machine learning in surgery include image interpretation, data summarization, automated narrative construction, trajectory and risk prediction, and operative navigation and robotics. The pace of development has been exponential, and some AI applications are working well. However, demonstrations of clinical utility, validity, and equity have lagged algorithm development and limited widespread adoption of AI into clinical practice. Outdated computing infrastructure and regulatory challenges which promote data silos are key barriers. Multidisciplinary teams will be needed to address these challenges and to build AI systems that are relevant, equitable, and dynamic.

关键词： artificial intelligence AI machine learning ML Computer vision Prediction Deep Learning Computer-aided diagnosis Augmented realtiy AR Surgery

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Transferring artificial intelligence practices between collaborative robotics and autonomous driving

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KYBERNETES 2023年第9期52卷 2924-2942页

作者： Zorman, Milan Zlahtic, Bojan Stradovnik, Sasa Hace, Ales Univ Maribor Fac Elect Engn & Comp Sci Inst Comp Sci Maribor Slovenia Univ Maribor Fac Elect Engn & Comp Sci Inst Robot Maribor Slovenia

Purpose Collaborative robotics and autonomous driving are fairly new disciplines, still with a long way to go to achieve goals, set by the research community, manufacturers and users. For technologies like collaborative robotics and autonomous driving, which focus on closing the gap between humans and machines, the physical, psychological and emotional needs of human individuals becoming increasingly important in order to ensure effective and safe human-machine interaction. The authors' goal was to conceptualize ways to combine experience from both fields and transfer artificial intelligence knowledge from one to another. By identifying transferable meta-knowledge, the authors will increase quality of artificial intelligence applications and raise safety and contextual awareness for users and environment in both fields. Design/methodology/approach First, the authors presented autonomous driving and collaborative robotics and autonomous driving and collaborative robotics' connection to artificial intelligence. The authors continued with advantages and challenges of both fields and identified potential topics for transferrable practices. Topics were divided into three time slots according to expected research timeline. Findings The identified research opportunities seem manageable in the presented timeline. The authors' expectation was that autonomous driving and collaborative robotics will start moving closer in the following years and even merging in some areas like driverless and humanless transport and logistics. Originality/value The authors' findings confirm the latest trends in autonomous driving and collaborative robotics and expand them into new research and collaboration opportunities for the next few years. The authors' research proposal focuses on those that should have the most positive impact to safety, complement, optimize and evolve human capabilities and increase productivity in line with social expectations. Transferring meta-knowledge between fields

关键词： robotics artificial intelligence Automation Decision-making Intelligent agents

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Control Techniques for vision-Based Autonomous Vehicles for Agricultural applications: A Meta-analytic Review 1

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International Conference on artificial intelligence: Theory and applications (AITA)

作者： Thakur, Abhishek Kumar, Ankit Mishra, Sudhansu Kumar BIT Mesra Ranchi 835215 Jharkhand India

ISBN: (数字)9789819984763

ISBN: (纸本)9789819984756;9789819984763

The agriculture industry continues to expand, showing significant potential in meeting the escalating food demand. Renowned for its plentiful resources and substantial agricultural yield, India is progressively leveraging advancements in technology for mass production of superior quality goods. Notably, the integration of robotics has revolutionized farming practices, encompassing activities such as fruit picking, sheep herding, pruning, weeding, spraying, and other farming tasks. The move toward automation not only mitigates the reliance on human labor but also enhances the overall productivity of the agriculture sector. This paper conducts a detailed examination of the progress in the realm of vision-based autonomous vehicles (AV) tailored for agricultural applications. Previous research has employed a range of machine learning algorithms, from linear and logistic regression, decision trees, support vector machines (SVM), naive Bayes, K-nearest neighbor (KNN) algorithms, to K-means. Nowadays, hybrid algorithms are increasingly employed in AV designs, addressing the constraints of single algorithms while elevating their efficiency. Furthermore, ensemble techniques are being used for efficient handling and accurate prediction over vast data quantities. These techniques amalgamate the predictive prowess of numerous base estimators, boosting robustness, and are predominantly employed today utilizing Python and R codes.

关键词： Autonomous vehicles artificial intelligence vision-based control machine learning algorithms Control techniques

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Opportunities and Challenges to Integrate artificial intelligence Into Manufacturing Systems: Thoughts From a Panel Discussion

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IEEE robotics & AUTOMATION MAGAZINE 2023年第2期30卷 109-112页

作者： Kovalenko, Ilya Barton, Kira Moyne, James Tilbury, Dawn M. Penn State Univ Dept Mech Engn University Pk PA 16802 USA Penn State Univ Dept Ind & Mfg Engn University Pk PA 16802 USA Univ Michigan Robot Dept Ann Arbor MI 48109 USA

Rapid advances in artificial intelligence (AI) have the potential to significantly increase productivity, quality, and profitability in future manufacturing systems. (Caveat: The panel did not attempt to disentangle artificial intelligence from machine learning and used the two terms loosely interchangeably during the discussion.) Traditional mass production will give way to personalized production, with each item made to order, at the low cost and high quality consumers have come to expect. Manufacturing systems will have the intelligence to be resilient to multiple disruptions, from small-scale machine breakdowns to large-scale natural disasters. Products will be made with higher precision and lower variability. While gains have been made toward the development of these factories of the future, many challenges remain to fully realize the vision shown in Figure 1.

关键词： artificial intelligence Manufacturing Manufacturing systems Industries Government Collaboration Robot kinematics

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A Comprehensive Survey of Convolutions in Deep Learning: applications, Challenges, and Future Trends

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IEEE ACCESS 2024年 12卷 41180-41218页

作者： Younesi, Abolfazl Ansari, Mohsen Fazli, Mohammadamin Ejlali, Alireza Shafique, Muhammad Henkel, Jorg Sharif Univ Technol Dept Comp Sci & Engn Tehran *** Iran New York Univ NYU Abu Dhabi Div Engn eBrainLab Abu Dhabi U Arab Emirates Karlsruhe Inst Technol KIT Dept Comp Sci D-76131 Karlsruhe Germany

In today's digital age, Convolutional Neural Networks (CNNs), a subset of Deep Learning (DL), are widely used for various computer vision tasks such as image classification, object detection, and image segmentation. There are numerous types of CNNs designed to meet specific needs and requirements, including 1D, 2D, and 3D CNNs, as well as dilated, grouped, attention, depthwise convolutions, and NAS, among others. Each type of CNN has its unique structure and characteristics, making it suitable for specific tasks. It's crucial to gain a thorough understanding and perform a comparative analysis of these different CNN types to understand their strengths and weaknesses. Furthermore, studying the performance, limitations, and practical applications of each type of CNN can aid in the development of new and improved architectures in the future. We also dive into the platforms and frameworks that researchers utilize for their research or development from various perspectives. Additionally, we explore the main research fields of CNN like 6D vision, generative models, and meta-learning. This survey paper provides a comprehensive examination and comparison of various CNN architectures, highlighting their architectural differences and emphasizing their respective advantages, disadvantages, applications, challenges, and future trends.

关键词： Deep learning DNN CNN machine learning vision transformers GAN attention computer vision LLM large language model transformer dilated convolution depthwise,NAS,NAT object detection 6D vision vision language model

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The emerging paradigm in pediatric rheumatology: harnessing the power of artificial intelligence

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RHEUMATOLOGY INTERNATIONAL 2024年第11期44卷 2315-2325页

作者： Koker, Oya Sahin, Sezgin Yildiz, Mehmet Adrovic, Amra Kasapcopur, Ozgur Marmara Univ Fac Med Dept Pediat Rheumatol Istanbul Turkiye Istanbul Univ Cerrahpasa Cerrahpasa Fac Med Dept Pediat Rheumatol Istanbul Turkiye

artificial intelligence algorithms, with roots extending into the past but experiencing a resurgence and evolution in recent years due to their superiority over traditional methods and contributions to human capabilities, have begun to make their presence felt in the field of pediatric rheumatology. In the ever-evolving realm of pediatric rheumatology, there have been incremental advancements supported by artificial intelligence in understanding and stratifying diseases, developing biomarkers, refining visual analyses, and facilitating individualized treatment approaches. However, like in many other domains, these strides have yet to gain clinical applicability and validation, and ethical issues remain unresolved. Furthermore, mastering different and novel terminologies appears challenging for clinicians. This review aims to provide a comprehensive overview of the current literature, categorizing algorithms and their applications, thus offering a fresh perspective on the nascent relationship between pediatric rheumatology and artificial intelligence, highlighting both its advancements and constraints.

关键词： artificial intelligence Computer vision systems Convolutional neural network Deep learning machine learning Pediatric rheumatology

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Neuro-symbolic artificial intelligence: a survey

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Neural Computing and applications 2024年第21期36卷 12809-12844页

作者： Bhuyan, Bikram Pratim Ramdane-Cherif, Amar Tomar, Ravi Singh, T.P. LISV Laboratory University of Paris Saclay 10-12 Avenue of Europe Velizy78140 France School of Computer Science University of Petroleum and Energy Studies Uttarakhand Bidholi Dehradun248006 India Persistent Systems Pune Mumbai411016 India School of Computer Science Engineering and Technology Bennett University Greater Noida201306 India

The goal of the growing discipline of neuro-symbolic artificial intelligence (AI) is to develop AI systems with more human-like reasoning capabilities by combining symbolic reasoning with connectionist learning. We survey the literature on neuro-symbolic AI during the last two decades, including books, monographs, review papers, contribution pieces, opinion articles, foundational workshops/talks, and related PhD theses. Four main features of neuro-symbolic AI are discussed, including representation, learning, reasoning, and decision-making. Finally, we discuss the many applications of neuro-symbolic AI, including question answering, robotics, computer vision, healthcare, and more. Scalability, explainability, and ethical considerations are also covered, as well as other difficulties and limits of neuro-symbolic AI. This study summarizes the current state of the art in neuro-symbolic artificial intelligence. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： machine learning

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