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共 434 条记录，以下是31-40 订阅

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Experimental Evaluation of Monocular vision-Based Obstacle Detection Methods

Experimental Evaluation of Monocular Vision-Based Obstacle D...

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Electrical, computer, Telecommunication and Energy Technologies (ECTE-Tech), International conference on

作者： Billel Nebili Rabah Louali Samir Sakhi Aimen Laidoudi Abdennour Youcefi Atmane Khellal Mehrez Boulahmar Ecole Militaire Polytechnique (EMP) Algiers Algeria

ISBN: (数字)9798350388480

ISBN: (纸本)9798350388497

Autonomous mobile robots require efficient and robust obstacle detection for safe and effective navigation in real-world environments. This paper explores the landscape of obstacle detection techniques, particularly monocular vision methods, which have garnered significant interest due to their affordability, lightweight nature, and rapid processing capabilities. In this work, we have investigated the performances of four practical monocular vision-based methods for obstacle detection: Canny edge detection, Shi-Tomasi corner detection, Otsu thresholding, and the You Only Look Once (YOLO) object detection model. We have evaluated these methods across diverse scenarios, considering variations in environmental conditions, camera placement, and obstacle types. The findings highlight the strengths and weaknesses of each approach, offering valuable insights for selecting appropriate techniques for robust obstacle detection.

关键词： YOLO Learning systems Deep learning Navigation Image edge detection Computational modeling robot vision systems Real-time systems Mobile robots Corner detection

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Colour based Object Classification using KNN Algorithm for Industrial Applications 1

Colour based Object Classification using KNN Algorithm for I...

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1st IEEE International conference on Automation, Computing and Renewable Systems, ICACRS 2022

作者： Santosh Kumar, N.C. Uma Maheswari, S. Vigneshwari, M. Pramila, P.V. Khilar, Rashmita Kumar, Ashok Department of Computer Science Engineering Kakatiya Institute of Technology and Science Telangana Warangal India Department of Electronics and Communication Engineering Panimalar Engineering College India Department of Information Technology Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha Schoolof Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India Department of Information Technology Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India Department of Computer Science Banasthali Vidyapith Rajasthan India

ISBN: (纸本)9781665460842

Object classification and detection involve numerous applications like image processing, picture retrieval, security and surveillance, video communication, robot vision and observation. They are often classified based on their properties like colour, shape, quality and texture. However, their accuracy is mostly predicted by colour recognition. Colour based detection of objects has a discriminating quality to detect an object of any primary colour. The extraction of two or three-dimensional objects based on colour plays a vital role in real-time image processing technology. This review paper proposes the method for colour-based object classification applied in the lays industry using the 'K-nearest neighbour algorithm' which is the simplest form of machine learning classification method. The ML model is designed in such a way that once the colour of the lays pack is detected it also specifies its particular flavour. Hence for efficient machine-based colour identification in industries, this method would be an appropriate one and can be used for many other applications. © 2022 IEEE

关键词： Classification (of information)

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Quadruped robot System Based on Proprioceptive vision and Complex Ground Mobility Capabilities 1st

Quadruped Robot System Based on Proprioceptive Vision and Co...

引用

1st International conference on Pattern Analysis and Machine Intelligence, ICPAMI 2024

作者： Ling, Xufeng Yuan, Hao Li, Sirui Tang, Bin Shanghai Normal University Tianhua College Shanghai China Royal College of Art Shanghai China Shanghai Zhiliang Electronic Technology Co. Ltd. Shanghai China

ISBN: (纸本)9789819633487

The purpose of this paper is to explore how to improve the motion accuracy and adaptability of quadruped robots in complex environments through deep reinforcement learning techniques. A proximal policy optimization (PPO) algorithm and a gated recurrent unit (GRU) network model are adopted, and the system is simulated and trained on NVIDIA Isaac Gym simulation platform. The experimental results show that the deep reinforcement learning strategy combining the PPO algorithm and the GRU model significantly improves the accuracy and adaptability of the quadruped robot in complex environments, which provides a new idea for the application of quadruped robots in extreme scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep reinforcement learning

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Hierarchical Aggregation for 3D Instance Segmentation 18

Hierarchical Aggregation for 3D Instance Segmentation

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18th IEEE/CVF International conference on computer vision (ICCV)

作者： Chen, Shaoyu Fang, Jiemin Zhang, Qian Liu, Wenyu Wang, Xinggang Huazhong Univ Sci & Technol Sch EIC Wuhan Peoples R China Huazhong Univ Sci & Technol Inst AI Wuhan Peoples R China Horizon Robot Beijing Peoples R China

ISBN: (纸本)9781665428125

Instance segmentation on point clouds is a fundamental task in 3D scene perception. In this work, we propose a concise clustering-based framework named HAIS, which makes full use of spatial relation of points and point sets. Considering clustering-based methods may result in oversegmentation or under-segmentation, we introduce the hierarchical aggregation to progressively generate instance proposals, i.e., point aggregation for preliminarily clustering points to sets and set aggregation for generating complete instances from sets. Once the complete 3D instances are obtained, a sub-network of intra-instance prediction is adopted for noisy points filtering and mask quality scoring. HAIS is fast (only 410ms per frame on Titan X)) and does not require non-maximum suppression. It ranks 1st on the ScanNet v2 benchmark 1, achieving the highest 69:9% AP50 and surpassing previous state-of-the-art (SOTA) methods by a large margin. Besides, the SOTA results on the S3DIS dataset validate the good generalization ability. Code is available at https://***/hustvl/HAIS.

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The First vision For Vitals (V4V) Challenge for Non-Contact Video-Based Physiological Estimation 18

The First Vision For Vitals (V4V) Challenge for Non-Contact ...

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IEEE/CVF International conference on computer vision (ICCVW)

作者： Revanur, Ambareesh Li, Zhihua Ciftci, Umur A. Yin, Lijun Jeni, Laszlo A. Carnegie Mellon Univ Robot Inst Pittsburgh PA 15213 USA SUNY Binghamton Dept Comp Sci Binghamton NY 13902 USA

ISBN: (纸本)9781665401913

Telehealth has the potential to offset the high demand for help during public health emergencies, such as the COVID-19 pandemic. Remote Photoplethysmography (rPPG) - the problem of non-invasively estimating blood volume variations in the microvascular tissue from video - would be well suited for these situations. Over the past few years a number of research groups have made rapid advances in remote PPG methods for estimating heart rate from digital video and obtained impressive results. How these various methods compare in naturalistic conditions, where spontaneous behavior, facial expressions, and illumination changes are present, is relatively unknown. To enable comparisons among alternative methods, the 1st vision for Vitals Challenge (V4V) presented a novel dataset containing high-resolution videos time-locked with varied physiological signals from a diverse population. In this paper, we outline the evaluation protocol, the data used, and the results. V4V is to be held in conjunction with the 2021 International conference on computer vision(1).

关键词： Measurement computer vision Protocols Sociology Estimation Benchmark testing Photoplethysmography

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Artificial Intelligence Algorithms for robotic Harvesting of Agricultural Produce

Artificial Intelligence Algorithms for Robotic Harvesting of...

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AIML-Applications for Engineering & Technology (ICAET), International conference on

作者： Nilesh R. Kolhalkar Anupama A. Pandit Shridhar Ashok Kedar G. Yedukondalu Department of Mechanical Engineering MKSSS's Cummins College of Engg. For Women Pune MH India Department of Computer Science & Engg. Defence Institute of Advanced Technology (D.I.A.T) Pune Pune MH India Department of Mechanical Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Vijayawada A.P. India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

robotic harvesting of fruits and vegetables is an advanced technology that leverages robotics, Artificial Intelligence, and Machine vision to harvest the fruits autonomously from plants or trees. This technology aims to address labor shortages, enhance efficiency, reduce costs, and minimize damage to the fruit during harvesting. AI algorithms for fruit detection and harvesting are increasingly used in agricultural automation to improve efficiency and accuracy. The accuracy of detection algorithms in fruit detection and harvesting can differ reliant on various factors, including the type of algorithm used, the quality and diversity of the training data, the complexity of the environment, and the specific fruits being targeted. Advanced control algorithms integrated with image processing ensure that the robotic arm moves smoothly and accurately, minimizing the risk of bruising or damaging the fruit. Soft robotics and adaptive gripping technologies are discussed in the paper which can handle delicate fruits like grapes, without applying excessive force. Machine vision integrated robot arm with novel gripper and cutter for harvesting cluster fruit like grapes is reported in the paper. Case studies of agricultural robots for Orchards, Greenhouses and Field Crops are discussed with detailed analysis along with challenges, future trends and innovations.

关键词： Accuracy Costs Automation Machine vision Pipelines Lighting Training data robot sensing systems Prediction algorithms robots

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RGB-D Videos Human Action Recognition: A Taxonomy-Based Survey to Learn the Discriminative Features

RGB-D Videos Human Action Recognition: A Taxonomy-Based Surv...

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Cognitive, Green and Ubiquitous Computing (IC-CGU), International conference on

作者： Girish Padhan Electrical & Electronics Engineering Vikash Institute of Technology (Biju patnaik University of Technology) Bargarh Odisha India

ISBN: (数字)9798350361780

ISBN: (纸本)9798350361797

In computer vision human action recognition plays an important role in the modern era. Here a sensor can acquired accurately the human action and their interactions from a previously unseen data sequence. Human activity identification in video sequences is a hotspot for computer vision research owing to its practical implications. It encompasses security, surveillance, healthcare, robotics, animation, sports analysis, smart home automation and behavioral analysis. The goal of AI society is to create a system that can observe and understand human behavior and actions completely independently. For example, a robot assistant might aid a patient undergoing home monitoring by analyzing the most effective method of exercise and so avoiding additional injuries, thereby increasing the robot's usefulness to society. This kind of smart technology will be immensely useful to us since it eliminates the need for unnecessary doctor visits, lowers healthcare costs, and allows for constant remote monitoring of the patient. Many feature-based methods, both manually designed and automatically taught, have emerged in the past two decades for identifying human actions in video footage. Traditional methods of human activity identification rely on meticulously designed characteristics that drill down to the most fundamental of motions. Over the last several years, deep learning algorithms have made great progress in a number of different domains, including human location prediction, object recognition, segmentation, audio analysis, object tracking, and super-resolution. In visual recognition tasks, the deep learning model is also crucial. Instead of manually extracting the characteristics, deep learning-based methods provide a more efficient and time-saving alternative. Handcrafted features solutions were shown to be effective, however they over-relied on feature descriptors when attempting action categorization. This type of problem needed additional man hours and specialized knowledge

关键词： Deep learning computer vision Visualization Medical services robot sensing systems Feature extraction Behavioral sciences

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Human Detection System

Human Detection System

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Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI), International conference on

作者： S. Muthuselvan S Harinisree B Mohanapriya R Sahana Information Technology department KCG College of Technology Tamil Nadu India

ISBN: (数字)9798350355338

ISBN: (纸本)9798350355345

The goal of this project is to develop a human detection robot that may be used as a backup mechanism to save lives in case of an emergency. Developing dependable surveillance systems is essential for both safety and security. The human detection model is a fundamental part of any surveillance system. Recent developments in embedded systems and hardware make it feasible to create a low-cost real-time person detecting system. This study investigates the architecture of human detection and tracking techniques over non-overlapping field of views. A search technique is presented to make up for each of these shortcomings. In an experimental context, the rate and accuracy of human detection using the suggested technique was evaluated.

关键词： Wireless communication computer vision Wireless sensor networks Accuracy Surveillance Real-time systems Sensors Safety Security Testing

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Categorical Depth Distribution Network for Monocular 3D Object Detection

Categorical Depth Distribution Network for Monocular 3D Obje...

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IEEE/CVF conference on computer vision and Pattern Recognition (CVPR)

作者： Reading, Cody Harakeh, Ali Chae, Julia Waslander, steven L. Univ Toronto Robot Inst Toronto ON Canada

ISBN: (纸本)9781665445092

Monocular 3D object detection is a key problem for autonomous vehicles, as it provides a solution with simple configuration compared to typical multi-sensor systems. The main challenge in monocular 3D detection lies in accurately predicting object depth, which must be inferred from object and scene cues due to the lack of direct range measurement. Many methods attempt to directly estimate depth to assist in 3D detection, but show limited performance as a result of depth inaccuracy. Our proposed solution, Categorical Depth Distribution Network (CaDDN), uses a predicted categorical depth distribution for each pixel to project rich contextual feature information to the appropriate depth interval in 3D space. We then use the computationally efficient bird's-eye-view projection and single-stage detector to produce the final output detections. We design CaDDN as a fully differentiable end-to-end approach for joint depth estimation and object detection. We validate our approach on the KIM 3D object detection benchmark, where we rank 1st among published monocular methods. We also provide the first monocular 3D detection results on the newly released Waymo Open Dataset. We provide a code release for CaDDN which is made available.

关键词： computer vision Three-dimensional displays Codes Estimation Object detection Distribution networks Detectors

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Sensor Fusion for Mobile robot Localization Using Extended Kalman Filter, UWB ToF and ArUco Markers 1st

Sensor Fusion for Mobile Robot Localization Using Extended K...

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1st International conference on Optimization, Learning Algorithms and Applications (OL2A)

作者： Faria, Silvia Lima, Jose Costa, Paulo Univ Porto Fac Engn Porto Portugal Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot Braganca Portugal INESC TEC INESC Technol & Sci Porto Portugal

ISBN: (纸本)9783030918859;9783030918842

The ability to locate a robot is one of the main features to be truly autonomous. Different methodologies can be used to determine robots location as accurately as possible, however these methodologies present several problems in some circumstances. One of these problems is the existence of uncertainty in the sensing of the robot. To solve this problem, it is necessary to combine the uncertain information correctly. In this way, it is possible to have a system that allows a more robust localization of the robot, more tolerant to failures and disturbances. This paper evaluates an Extended Kalman Filter (EKF) that fuses odometry information with Ultra-WideBand Time-of-Flight (UWB ToF) measurements and camera measurements from the detection of ArUco markers in the environment. The proposed system is validated in a real environment with a differential robot developed for this purpose, and the achieved results are promising.

关键词： ArUco markers Autonomous mobile robot Extended kalman filter Localization Ultra-WideBand vision based system

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