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MaRMOT: A Modular and Reconfigurable Multiple Object Tracking Framework for robots and intelligent Systems 33

MaRMOT: A Modular and Reconfigurable Multiple Object Trackin...

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33rd IEEE International conference on Robot and Human Interactive Communication (IEEE RO-MAN) - Embracing Human-Centered HRI

作者： Duncan, John A. Alambeigi, Farshid Pryor, Mitchell W. Univ Texas Austin Walker Dept Mech Engn Austin TX 78712 USA

ISBN: (纸本)9798350375039;9798350375022

Multiple object tracking (MOT) is a valuable perception function for robots and intelligent systems. Despite rapid improvements in metrics such as Average Multiple Object Tracking Accuracy (AMOTA) on MOT benchmarks, many trackers are application-specific or run at speeds <1 frames per second (FPS) making them impractical for dynamic tracking applications such as human-robot interaction. To this end, we introduce Modular and Reconfigurable Multiple Object Tracking (MaRMOT), a general-purpose tracking framework for robots implemented in ROS2. Using the nuScenes MOT development kit, we provide accuracy and speed metrics (AMOTA, average FPS, and worst-case FPS) for various tracking methods. We achieve AMOTA of 50.5% with an average of 62.0 FPS on the nuScenes test split, making MaRMOT suitable for online tracking in complex scenes. We demonstrate MaRMOT's modularity on two human tracking applications with different hardware configurations: a 2x object detection camera "smart space" configuration, and a mobile robot configuration with 3D LiDAR detector and an object detection camera. We show that an alternate sensor configuration using human position measurements from a headset improves multiple object tracking accuracy (MOTA) over vision and LiDAR detection alone. Finally, we provide software and hardware design recommendations for tracking applications with tracker speed requirements >10 FPS. MaRMOT is open source and can be extended with new detectors, process models, matching algorithms, and track management techniques.

关键词： Open source software

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Pay Self-Attention to Audio-Visual Navigation 33

Pay Self-Attention to Audio-Visual Navigation

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33rd British Machine vision conference Proceedings, BMVC 2022

作者： Yu, Yinfeng Cao, Lele Sun, Fuchun Liu, Xiaohong Wang, Liejun Department of Computer Science and Technology State Key Lab on Intelligent Technology and Systems Tsinghua University Beijing China Motherbrain EQT Stockholm Sweden College of Information Science and Engineering Xinjiang University Urumqi China

Audio-visual embodied navigation, as a hot research topic, aims training a robot to reach an audio target using egocentric visual (from the sensors mounted on the robot) and audio (emitted from the target) input. The audio-visual information fusion strategy is naturally important to the navigation performance, but the state-of-the-art methods still simply concatenate the visual and audio features, potentially ignoring the direct impact of context. Moreover, the existing approaches requires either phase-wise training or additional aid (e.g. topology graph and sound semantics). Up till this date, the work that deals with the more challenging setup with moving target(s) is still rare. As a result, we propose an end-to-end framework FSAAVN (feature self-attention audio-visual navigation) to learn chasing after a moving audio target using a context-aware audio-visual fusion strategy implemented as a self-attention module. Our thorough experiments validate the superior performance (both quantitatively and qualitatively) of FSAAVN in comparison with the state-of-the-arts, and also provide unique insights about the choice of visual modalities, visual/audio encoder backbones and fusion patterns. © 2022. The copyright of this document resides with its authors.

关键词： robots

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Human-computer Interaction in Artificial Intelligence for Blind and vision Impairment: An Interpretative Literature Review Based on Bibliometrics 33

Human-Computer Interaction in Artificial Intelligence for Bl...

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33e conference Internationale Francophone sur l'Interaction Homme-Machine: Interaction Humain and IA, IHM 2022 - 33rd International French-Speaking conference on Human-computer Interaction: Human Interaction and AI, IHM 2022

作者： Jeanneret Medina, Maximiliano Lalanne, Denis Baudet, Cédric HUMAN-IST University of Fribourg Switzerland Heg Arc HES-SO University of Applied Sciences Western Switzerland Switzerland

ISBN: (纸本)9781450391986

The rise of artificial intelligence and particularly machine learning conduct to an emerging landscape of intelligent interactive systems. Such technologies help clinicians to detect diseases from medical imaging, and allow to describe the visual world to people with visual impairment. However, this new technological landscape comes with a set of HCI challenges. To better understand the importance of HCI in AI, we focused on blind and vision impairment as a representative application domain. Using bibliometric techniques, we retained 187 scientific publications organized in three clusters. Our findings show that HCI is absent in research related to medical computer systems but has moderate importance when the aim is to assist BVI in their daily life. © 2022 Owner/Author.

关键词： Human computer interaction

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MaRMOT: A Modular and Reconfigurable Multiple Object Tracking Framework for robots and intelligent Systems

MaRMOT: A Modular and Reconfigurable Multiple Object Trackin...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： John A. Duncan Farshid Alambeigi Mitchell W. Pryor Walker Department of Mechanical Engineering The University of Texas at Austin Austin TX USA

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

Multiple object tracking (MOT) is a valuable perception function for robots and intelligent systems. Despite rapid improvements in metrics such as Average Multiple Object Tracking Accuracy (AMOTA) on MOT benchmarks, many trackers are application-specific or run at speeds <1 frames per second (FPS) making them impractical for dynamic tracking applications such as human-robot interaction. To this end, we introduce Modular and Reconfigurable Multiple Object Tracking (MaRMOT), a general-purpose tracking framework for robots implemented in ROS2. Using the nuScenes MOT development kit, we provide accuracy and speed metrics (AMOTA, average FPS, and worst-case FPS) for various tracking methods. We achieve AMOTA of 50.5% with an average of 62.0 FPS on the nuScenes test split, making MaRMOT suitable for online tracking in complex scenes. We demonstrate MaRMOT’s modularity on two human tracking applications with different hardware configurations: a 2x object detection camera "smart space" configuration, and a mobile robot configuration with 3D LiDAR detector and an object detection camera. We show that an alternate sensor configuration using human position measurements from a headset improves multiple object tracking accuracy (MOTA) over vision and LiDAR detection alone. Finally, we provide software and hardware design recommendations for tracking applications with tracker speed requirements >10 FPS. MaRMOT is open source and can be extended with new detectors, process models, matching algorithms, and track management techniques.

关键词： Measurement Accuracy Laser radar Robot vision systems Object detection Detectors Cameras Hardware Object tracking intelligent systems

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Novel 6D Pose Estimation for Pick-and-Place Tasks using Model-based Learning

Novel 6D Pose Estimation for Pick-and-Place Tasks using Mode...

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intelligent Data Communication Technologies and Internet of Things (IDCIoT), International conference on

作者： Logesh G S. Vinoth Nagarajan Pitchandi A.G. Karthikeyan Computer Science and Business Systems Bannari Amman Institute of Technology Sathyamangalam India Department of Mechatronics Akshaya College of Engineering and Technology Coimbatore India Department of Mechatronics Bannari Amman Institute of Technology Sathyamangalam India School of Mechanical Engineering REVA University Bangalore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The paper discusses the problem of creating an autonomous SCARA robot that can precisely carry out pick-and-place operations in unstructured settings. The suggested method combines computer vision and deep learning algorithms to provide accurate 6D pose estimation and reliable object detection. Combining YOLOv8 for object detection, hand-eye calibration for precise transformation to robot coordinates, and Principal Component Analysis (PCA) for orientation estimation within the generated point cloud, a novel method for estimating object pose is presented. Monocular depth estimation techniques are used to extract depth information from RGB images to create point clouds. The results demonstrate the potential of combining advanced algorithms with user-friendly design for robotics automation, showing notable gains in pose estimation accuracy and task execution efficiency.

关键词： Point cloud compression Accuracy Service robots Robot kinematics Pose estimation Object detection Manuals Calibration Reliability Principal component analysis

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Comparative Analysis of computer vision techniques for Traffic Signs Recognition in Autonomous Vehicles

Comparative Analysis of Computer Vision Techniques for Traff...

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International conference on computer Theory and Applications (ICCTA)

作者： Yaman Sahu Mahmoud Wael Bibek Gupta Tasneem Tarek Abdelkareem Mohamed A. Wahby Shalaby EU4M Mechatronics Program (MECT) School of Engineering and Applied Sciences Nile University 6th October Egypt Mechatronics Program (MECT) School of Engineering and Applied Sciences Nile University 6th October Egypt Information Technology Dept. Faculty of Computers and Artificial Intelligence Cairo University Giza Egypt Smart Engineering Systems Research Center (SESC) Nile University 6th October Egypt

ISBN: (数字)9798350309584

ISBN: (纸本)9798350309591

The popularity of self-driving vehicles has been steadily increasing due to advancements in technology. These vehicles, often referred to as autonomous vehicles, rely on a combination of sensors, cameras, and artificial intelligence to operate. Cameras have multiple applications in autonomous vehicles, including traffic sign detection and processing. This study aims to compare the performance of three computer vision techniques - SIFT (Scale Invariant Feature Transform), ORB (Oriented FAST and Rotated BRIEF), and CNN (Convolutional Neural Network) - on the same data set using precision, recall and F1-Score as metrics. The experimental results of this study indicate that CNN outperforms SIFT and ORB in terms of all mentioned metrics.

关键词： Measurement computer vision Transforms Cameras Convolutional neural networks Artificial intelligence Autonomous vehicles intelligent sensors

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Fusing Components for an Attentive and Emotionally Expressive Companion Robot: Meet ZENIT

Fusing Components for an Attentive and Emotionally Expressiv...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Christian Felix Purps Matthias Wölfel Institute for Intelligent Interaction and Immersive Experience Karlsruhe University of Applied Sciences Karlsruhe Germany University of Hohenheim Stuttgart Germany

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

This paper introduces a new companion robot as an open research platform for studying human-robot interaction: The ZENIT Enabling Natural Interaction Technology. Open-source solutions for companion robots are often complicated to build or not thought of holistically. ZENIT is based on a few low-cost components, namely a smartphone and a stationary robot arm in conjunction with an average computer and a separate camera. This concept makes it possible not to be tied to specific hardware. Our platform aims to enable easy use of the multimodality of human communication channels such as body language, spatial behavior, facial expressions and speech so that ZENIT can understand its interlocutor and express itself appropriately. The fusion of various open-source software components in an adaptable, lightweight and resource-efficient distributed system simplifies its replication. The system demonstrated an adequate response time (speech: 2.69s, facial expressions: 0.55s, bodily movements: 0.32s) for perception and expression combined, as well as reliability in public testing. Evaluation of the robot’s expressive capabilities in an online survey (N=23) revealed users were able to understand six of eight emotions conveyed by ZENIT using facial expressions and/or body language (except for contempt and fear) and perceived its non-verbal feedback as significantly more expressive when facial expressions were accompanied with bodily movements.

关键词： Surveys Robot vision systems Human-robot interaction Manipulators Hardware Software reliability Time factors robots Open source software Testing

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computer vision System for Weld Bead Geometric Analysis 18

Computer Vision System for Weld Bead Geometric Analysis

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33rd Annual ACM Symposium on Applied Computing (ACM SAC)

作者： Soares, Luciane B. Weis, Atila A. Guterres, Bruna de V. Rodrigues, Ricardo N. Botelho, Silvia S. da C. Fundacao Univ Fed Rio Grande Rio Grande Brazil Intelligent Robot & Automat Grp NAUTEC Rio Grande Brazil Sci Comp Ctr C3 Rio Grande Brazil

ISBN: (纸本)9781450351911

Welding is an important process in several industries and it is required precision and attention during the execution. In addition it is unhealthy work for the operator who is in direct contact with sparks, smoke and electricity. Thus, it is increasingly common to use robots in this type of task, reducing human intervention in the process. This work proposes an automated system to verify discontinuities in the weld beads, which may invalidate them. Through the use of a linear welding robot and a vision system, images of the pre-welding and post-welding metal plates are captured for future comparisons and evaluation metrics applied. The proposed method uses the combination of techniques such as: PCA, Gaussian Mixture and HMM with the objective of identifying the edges of the weld beads. Then, the proposed metrics are applied in order to assist the welding inspector to identify possible discontinuities in the weld.

关键词： computer vision Discontinuities Welding

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A study on tooth segmentation and numbering using end-to-end deep neural networks

A study on tooth segmentation and numbering using end-to-end...

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Brazilian Symposium on computer Graphics and Image Processing (SIBGRAPI)

作者： Bernardo Silva Laís Pinheiro Luciano Oliveira Matheus Pithon Intelligent Vision Research Lab Universidade Federal da Bahia Salvador Bahia Universidade Estadual do Sudoeste da Bahia Jequié Bahia

ISBN: (数字)9781728192741

ISBN: (纸本)9781728192758

Shape, number, and position of teeth are the main targets of a dentist when screening for patient's problems on X-rays. Rather than solely relying on the trained eyes of the dentists, computational tools have been proposed to aid specialists as decision supporter for better diagnoses. When applied to X-rays, these tools are specially grounded on object segmentation and detection. In fact, the very first goal of segmenting and detecting the teeth in the images is to facilitate other automatic methods in further processing steps. Although researches over tooth segmentation and detection are not recent, the application of deep learning techniques in the field is new and has not reached maturity yet. To fill some gaps in the area of dental image analysis, we bring a thorough study on tooth segmentation and numbering on panoramic X-ray images by means of end-to-end deep neural networks. For that, we analyze the performance of four network architectures, namely, Mask R-CNN, PANet, HTC, and ResNeSt, over a challenging data set. The choice of these networks was made upon their high performance over other data sets for instance segmentation and detection. To the best of our knowledge, this is the first study on instance segmentation, detection, and numbering of teeth on panoramic dental X-rays. We found that (i) it is completely feasible to detect, to segment, and to number teeth by through any of the analyzed architectures, (ii) performance can be significantly boosted with the proper choice of neural network architecture, and (iii) the PANet had the best results on our evaluations with an mAP of 71.3% on segmentation and 74.0% on numbering, raising 4.9 and 3.5 percentage points the results obtained with Mask R-CNN.

关键词： Teeth Dentistry Image segmentation X-rays Neural networks Detectors Deep learning

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Research and implementation of children's wear model based on improved BP algorithm 3

Research and implementation of children's wear model based o...

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3rd IEEE International conference on Electronic Information Technology and computer Engineering, EITCE 2019

作者： Liu, Ying Qiu, Defu Zheng, Lixin Huaqiao University Fujian Prov. Acad. Eng. Res. Ctr. in Indust. Intellectual Techniques and Syst. Coll. of Engineering Quanzhou China

ISBN: (纸本)9781728135847

In order to alleviate the extreme shortage, casual resignation of clothing designers, and the continuous improvement of the need for individual customization, we have come up with an improved BP algorithm which using children's easy-to-measure parts size data to forecast the size of complex parts required for different children's wear. We made and produced the predicted data to meet the actual requirements. This study compares the predicted results of the improved BP algorithm with those of the original BP algorithm and other algorithms, proving that the improved BP algorithm is with stronger stability, higher accuracy and faster convergence speed. The data required for pattern making is predicted by the improved BP algorithm, and the error between the forecast data and the actual data is as high as 99% within 0.3 cm, which is more advantageous than other algorithms in learning various clothing types and realizing monomer customization. © 2019 IEEE.

关键词： Children's wear Clothing designer Improved bP algorithm Individual customization Stability

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