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2nd International Conference on Inventive Computing and Informatics (ICICI)

作者： Prateek Tanvir, Afraz Brindha, R. SRM Inst Sci & Technol Dept Comp Technol Kattankulathur Chengalpattu India

ISBN: (纸本)9798350373301;9798350373295

The AI-powered exercise tracking system presented in this project represents a new approach to revolutionize fitness routines using cutting-edge technologies. Leveraging advanced computer vision techniques, particularly pose estimation, the system offers real-time tracking and analysis of exercise repetitions during gym workouts. Focused initially on four fundamental exercises like Bicep Curls, Push-Ups, Squats, and Shoulder Press. The proposed system employs Machine Learning (ML) models to detect and classify correct and incorrect exercise forms. Integrating the power of Mediapipe's pose detection framework and speech recognition capabilities, the proposed system provides accurate feedback and guidance to users through a smart web application interface. Through the selection of a tech stack including Python, OpenCV, Streamlit, and Scikit-Learn, the system ensures seamless data processing, model training, and user interaction. By analyzing workout videos uploaded by users, the proposed system identifies and highlights areas of improvement in exercise technique, enhancing the effectiveness and safety of fitness routines. Moreover, the integration of speech recognition enables hands-free control, adding convenience and accessibility for users during their workouts. This research study not only aims to optimize individual fitness journeys but also contributes to advancing the field of fitness tracking through the application of Artificial Intelligence (AI) and computer vision technologies. By empowering users with personalized feedback and guidance, the proposed system promotes healthier habits, improved performance, and overall well-being.

关键词： Human pose estimation Deep Learning Models Human Body pose Recognition Skeleton estimation Joint Localization pose Tracking Human Activity Recognition object detection and pose estimation

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Hybrid Deep Learning and FAST-BRISK 3D object detection Technique for Bin-picking Application

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SENSORS AND MATERIALS 2024年第4期36卷 1389-1404页

作者： Taweesoontorn, Thanakrit Yanyong, Sarucha Konghuayrob, Poom King Mongkuts Inst Technol Ladkrabang Sch Engn Dept Robot & AI Engn 1 Chalong Kung1 Alley Bangkok 10520 Thailand

In the field of industrial robotics, robotic arms have been significantly integrated, driven by their precise functionality and operational efficiency. We here propose a hybrid method for binpicking tasks using a collaborative robot, or cobot combining the You Only Look Once version 5 (YOLOv5) convolutional neural network (CNN) model for object detection and pose estimation with traditional feature detection based on the features from accelerated segment test (FAST) technique, feature description using binary robust invariant scalable keypoints (BRISK) algorithms, and matching algorithms. By integrating these algorithms and utilizing a low-cost depth sensor camera for capturing depth and RGB images, the system enhances real -time object detection and pose estimation speed, facilitating accurate object manipulation by the robotic arm. Furthermore, the proposed method is implemented within the robot operating system (ROS) framework to provide a seamless platform for robotic control and integration. We compared our results with those of other methodologies, highlighting the superior object detection accuracy and processing speed of our hybrid approach. This integration of robotic arm, camera, and AI technology contributes to the development of industrial robotics, opening up new possibilities for automating challenging tasks and improving overall operational efficiency.

关键词： computer vision object detection and pose estimation bin-picking application robot operating system

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An Improved Algorithm for detection and pose estimation of Texture-Less objects

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2021年第2期25卷 204-212页

作者： Peng, Jian Su, Ya China Univ Geosci Sch Automat 388 Lumo Rd Wuhan 430074 Hubei Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C 388 Lumo Rd Wuhan 430074 Hubei Peoples R China

This paper introduces an improved algorithm for texture-less object detection and pose estimation in industrial scenes. In the template training stage, a multi-scale template training method is proposed to improve the sensitivity of LineMOD to template depth. When this method performs template matching, the test image is first divided into several regions, and then training templates with similar depth are selected according to the depth of each test image region. In this way, without traversing all the templates, the depth of the template used by the algorithm during template matching is kept close to the depth of the target object, which improves the speed of the algorithm while ensuring that the accuracy of recognition will not decrease. In addition, this paper also proposes a method called coarse positioning of objects. The method avoids a lot of useless matching operations, and further improves the speed of the algorithm. The experimental results show that the improved LineMOD algorithm in this paper can effectively solve the algorithm's template depth sensitivity problem.

关键词： computer vision object detection and pose estimation LineMOD algorithm

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Autonomous Safety Barrier Inspection in Construction: An Approach Using Unmanned Aerial Vehicles and SafeBIM 38

Autonomous Safety Barrier Inspection in Construction: An App...

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38th International Symposium on Automation and Robotics in Construction, ISARC 2021

作者： Johansen, Karsten W. de Figueiredo, Rui Pimentel Golovina, Olga Teizer, Jochen Aarhus University Denmark

ISBN: (纸本)9789526952413

Construction sites are dynamic, and the environment is changing fast, which means the collective safety equipment, such as fall protection barriers, should also be changed to keep it compliant with the construction codes. However, any safety equipment can become non-compliant for several reasons, e.g., temporal removal in combination with incorrect or omitted re-installation or changes in the building process. Thus, there is a demand for frequent inspection of the equipment, which is time- and labor-intensive as this is currently done through manual examination by safety experts. In this work, we utilize an unmanned aerial vehicle (UAV) to detect the presence, absence, and defects of safety equipment in construction work-site environments. Furthermore, the UAV continuously inspects and provides safety object location information that human collaborators can use to improve safety within the environment. We utilize an 3D occupancy grid representation to map the environment and compact point pair feature representations for efficient and robust object recognition and pose estimation. To assess the applicability and accuracy of our methods for model-based pose estimation of Building Information Model (BIM) structures, we created a realistic, simulated construction environment. Our experiment demonstrates the applicability and precision of drone-aided localization and inspection of safety equipment in the construction industries. © 2021 Proceedings of the International Symposium on Automation and Robotics in Construction. All rights reserved.

关键词： augmented Reality (AR) Automation human robot collaboration (HRI) inspection object detection and pose estimation point clouds safety unmanned aerial systems

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Efficient collection and automatic annotation of real-world object images by taking advantage of post-diminished multiple visual markers

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ADVANCED ROBOTICS 2019年第24期33卷 1264-1280页

作者： Kiyokawa, Takuya Tomochika, Keita Takamatsu, Jun Ogasawara, Tsukasa Nara Inst Sci & Technol Div Informat Sci Nara Japan

To collect a human-annotated dataset for training deep convolutional neural networks is a very time-consuming and laborious process. To reduce this burden, we previously proposed an automated annotation by placing one visual marker above the detection target object in the training phase. However, in this approach, occasionally the marker hides the object surface. To avoid this issue, we propose placing a pedestal with multiple markers at the bottom of the object. If we use multiple markers, the object can be annotated even when the object hides some of the markers. Besides that, the simple modification of placing the markers on the bottom allows the use of simple background masking to avoid the neural network learning the remaining markers in the training image as a feature of the object. Background masking can completely remove the markers during the training process. Experiments showed the proposed vision system using our automatic object annotation outperformed the vision system using manual annotation in terms of object detection, orientation estimation, and 2D position estimation while reducing the time required for dataset collection from 16.1 hours to 7.30 hours.

关键词： Automatic annotation computer vision for automation deep learning in robotics and automation object detection and pose estimation

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Real-time object detection and tracking for industrial applications

Real-time object detection and tracking for industrial appli...

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3rd International Conference on Computer Vision Theory and Applications

作者： Benhimane, Selim Najafi, Hesam Grundmann, Matthias Genc, Yakup Navab, Nassir Malis, Ezio Tech Univ Munich Dept Comp Sci Boltzmannstr 3 D-85748 Garching Germany

ISBN: (纸本)9789898111210

Real-time tracking of complex 3D objects has been shown to be a challenging task for industrial applications where robustness, accuracy and run-time performance are of critical importance. This paper presents a fully automated object tracking system which is capable of overcoming some of the problems faced in industrial environments. This is achieved by combining a real-time tracking system with a fast object detection system for automatic initialization and re-initialization at run-time. This ensures robustness of object detection, and at the same time accuracy and speed of recursive tracking. For the initialization we build a compact representation of the object of interest using statistical learning techniques during an off-line learning phase, in order to achieve speed and reliability at run-time by imposing geometric and photometric consistency constraints. The proposed tracking system is based on a novel template management algorithm which is incorporated into the ESM algorithm. Experimental results demonstrate the robustness and high precision of tracking of complex industrial machines with poor textures under severe illumination conditions.

关键词： real-time vision template-based tracking object recognition object detection and pose estimation Augmented Reality

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