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2021 intelligent robotics and industrial applications using computer vision Conference, iriacv 2021

作者： Christ, Micha Jauch, Christian Denecke, Julia Wiedenroth, Saskia J. Department Image and Signal Processing Fraunhofer IPA Stuttgart Germany

Working in protected workshops places supervisor workers in a work field with concurrent targets. On the one side, the workers with disabilities require a safe space to meet special requirements and on the other side, customers expect comparable time and quality standards than in the normal industry while maintaining cost pressure. We propose a technical solution to support the supervisors with the quality control. We developed a flexible assistance system for people with disabilities working in protected workshops that is based on a Raspberry Pi4 and uses cameras for perception. It is appliable for packaging and picking processes and is supported by additional step by step guidance to reach as many protected workshops as possible. The system tries to support supervisors in quality control and provide information if any action is required to free time for interpersonal matters. An automatic pick-by-light system is included which uses hand recognition. To ensure good speed we used image processing and verified the detections with a machine learning approach for robustness against lighting conditions. In this paper we present the system, which is available open source, itself with its features and the development of the machine learning algorithm. © 2021 Society for Imaging Science and Technology. All rights reserved.

关键词： Supervisory personnel

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Concurrent two-factor identify verification using facial identify and facial actions

Concurrent two-factor identify verification using facial ide...

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2021 intelligent robotics and industrial applications using computer vision Conference, iriacv 2021

作者： Sun, Zheng Lee, Dah-Jye Zhang, Dong Li, Xiao Brigham Young University ProvoUT84602 United States Sun Yat-sen University Guangdong Guangzhou510006 China

Identity verification is ubiquitous in daily life. Its applications range from unlocking mobile device to accessing online account, boarding airplane or other types of transportation, recording times of arrival and leaving work, controlling access to a restricted area, facility, or vault, and many more. The traditional and the most popular identity verification is password authentication but with many challenges. Human biometric identifiers like fingerprint, retina scan, and 2D or 3D facial features have become popular alternatives. Some applications use two-factor or multi-factor authentication to increase system security, e.g., password and login code sent to a mobile device. All these identity verification methods have their challenges ranging from forgotten or stolen password to unaware or unintentional authentication and complexity and high costs. This paper presents a promising alternative that could be an improvement to the existing identity verification methods. This improved identity verification is a two-factor approach that concurrently analyzes facial features and unique facial actions. The user's facial features and facial actions must both match what have been stored in the system in order to pass identity verification. This two-factor verification requires only the frontal view of the face and authenticates facial features and facial actions concurrently. It generates an embedding of facial features and facial action in a short video for matching. We name this method Current Two-Factor Identity Verification (C2FIV). Two frameworks that use recurrent neural networks to learn the representation of facial features and actions. One uses an auto-encoder, and the other one uses metric learning. Experimental result shows that the metric learning model performs reliably with an average precision of 98.8%. © 2021, Society for Imaging Science and Technology.

关键词： Authentication

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Methods and comparisons between computer vision and radar based vehicle location

Methods and comparisons between computer vision and radar ba...

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2021 intelligent robotics and industrial applications using computer vision Conference, iriacv 2021

作者： Aykac, Deniz Ferrell, Regina Srinivas, Nisha Karnowski, Thomas Electrification and Energy Infrastructure Division Oak Ridge National Laboratory Oak RidgeTN37831 United States

Measuring vehicle locations relative to a driver's vehicle is a critical component in the analysis of driving data from both postanalysis (such as in naturalistic driving studies) or in autonomous vehicle navigation. In this work we describe a method to estimate vehicle positions from a forward-looking video camera using intrinsic camera calibration, estimates of extrinsic parameters, and a convolutional neural network trained to detect and locate vehicles in video data. We compare the measurements we achieve with this method with ground truth and with radar data available from a naturalistic driving study. We identify regions where video is preferred, where radar is preferred, and explore trade-offs between the two methods in regions where the preference is more ambiguous. We describe applications of these measurements for transportation analysis. © 2021, Society for Imaging Science and Technology.

关键词： Data fusion

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Towards large-scale evaluation of mental stress and biomechanical strain in manufacturing environments using 3D-referenced gaze and wearable-based analytics

Towards large-scale evaluation of mental stress and biomecha...

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2021 intelligent robotics and industrial applications using computer vision Conference, iriacv 2021

作者： Paletta, Lucas Ganster, Harald Schneeberger, Michael Pszeida, Martin Lodron, Gerald Pechstädt, Katrin Spitzer, Michaela Reischl, Christiane JOANNEUM RESEARCH Graz Austria FH JOANNEUM Bad Gleichenberg Graz Austria

In future manufacturing human-machine interaction will evolve towards flexible and smart collaboration. It will meet requirements from the optimization of assembly processes as well as from motivated and skilled human behavior. Recently, human factors engineering has substantially progressed by means of detailed task analysis. However, there is still a lack in precise measuring cognitive and sensorimotor patterns for the analysis of long-term mental and physical strain. This work presents a novel methodology that enables real-time measurement of cognitive load based on executive function analyses as well as biomechanical strain from non-obtrusive wearable sensors. The methodology works on 3D information recovery of the working cell using a precise stereo measurement device. The worker is equipped with eye tracking glasses and a set of wearable accelerometers. Wireless connectivity transmits the sensor-based data to a nearby PC for monitoring. Data analytics then recovers the 3D geometry of gaze and viewing frustum within the working cell and furthermore extracts the worker's task switching rate as well as a skeleton-based approximation of worker's posture associated with an estimation of biomechanical strain of muscles and joints. First results enhanced by AI-based estimators demonstrate a good match with the results of an activity analysis performed by occupational therapists. © 2021, Society for Imaging Science and Technology.

关键词： Eye tracking

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Tailored photometric stereo: Optimization of light source positions for various materials

Tailored photometric stereo: Optimization of light source po...

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2020 intelligent robotics and industrial applications using computer vision Conference, iriacv 2020

作者： Kapeller, Christian Antensteiner, Doris Štolc, Svorad AIT Austrian Institute of Technology GmbH High Performance Vision Vienna1210 Austria Vienna University of Technology Institute of Visual Computing Vienna1040 Austria

industrial machine vision applications frequently employ Photometric Stereo (PS) methods to detect fine surface defects on objects with challenging surface properties. To achieve highly precise results, acquisition setups with a vast amount of strobed illumination angles are required. The time-consuming nature of such an undertaking renders it inapt for most industrial applications. We overcome these limitations by carefully tailoring the required light setup to specific applications. Our novel approach facilitates the design of optimized acquisition setups for inline PS inspection systems. The optimal positions of light sources are derived from only a few representative material samples without the need for extensive amounts of training data. We formulate an energy function that constructs the illumination setup which generates the highest PS accuracy. The setup can be tailored for fast acquisition speed or cost efficiency. A thorough evaluation of the performance of our approach will be given on a public data set, evaluated by the mean angular error (MAE) for surface normals and root mean square (RMS) error for albedos. Our results show, that the obtained optimized PS setups can deliver a reconstruction performance close to the ground truth, while requiring only a few acquisitions. © 2020 Society for Imaging Science and Technology.

关键词： Light sources

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Crowd congestion detection in videos

Crowd congestion detection in videos

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2020 intelligent robotics and industrial applications using computer vision Conference, iriacv 2020

作者： Khan, Sultan Daud Mahmud, Maqsood Ullah, Habib Ullah, Mohib Cheikh, Faouzi Alaya College of Computer Science and Engineering University of Ha'il Saudi Arabia Department of Management Information System University of Imam Abdulrahman Bin Faisal Dammam Saudi Arabia Department of Computer Science Norwegian University of Science and Technology Norway

Automatic detection of crowd congestion in high density crowds is a challenging problem, with substantial interest for safety and security applications. In this paper, we propose a method that can automatically identify and localize congested regions in crowded videos. Our proposed method is based on the notion that pedestrians in the congested region follow a particular behavior. Pedestrians in the congested areas cannot move freely due to space unavailability and tend to undergo lateral oscillations. In our method, we first extract trajectories by using particle advection technique and then compute oscillatory features for each trajectory. Trajectories with higher oscillation values and with less proximity are clustered, indicating the congested regions. We perform experiments on a diversity of challenging scenarios. From the experimental results, we show that our method provides precise localization of congested regions in crowd videos. © 2020 Society for Imaging Science and Technology. All rights reserved.

关键词： Anomaly detection

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AR-based deep learning for real-time inspection of cable brackets in aircraft

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robotics AND computer-INTEGRATED MANUFACTURING 2023年第1期83卷

作者： Hu, Jingyu Zhao, Gang Xiao, Wenlei Li, Rupeng Beihang Univ Sch Mech Engn & Automat Beijing 100191 Peoples R China Beihang Univ MIIT Key Lab Aeronaut Intelligent Mfg Beijing 100191 Peoples R China Shanghai Aircraft Mfg Co Ltd Inst Aeronaut Mfg Technol Shanghai 200120 Peoples R China

In the process of aircraft assembly, there exist numerous and ubiquitous cable brackets that shall be installed on frames and subsequently need to be manually verified with CAD models. Such a task is usually performed by special operators, hence is time-consuming, labor-intensive, and error-prone. In order to save the inspection time and increase the reliability of results, many researchers attempt to develop intelligent inspection systems using robotic, AR, or AI technologies. However, there is no comprehensive method to achieve enough portability, intelligence, efficiency, and accuracy while providing intuitive task assistance for inspectors in real time. In this paper, a combined AR+AI system is introduced to assist brackets inspection in a more intelligent yet efficient manner. Especially, AR-based Mask R-CNN is proposed by skillfully integrating markerless AR into deep learning-based instance segmentation to generate more accurate and fewer region proposals, and thus alleviates the computation load of the deep learning program. Based on this, brackets segmentation can be performed robustly and efficiently on mobile devices such as smartphones or tablets. By using the proposed system, CAD model checking can be automatically performed between the segmented physical brackets and the corresponding virtual brackets rendered by AR in real time. Furthermore, the inspection results can be directly projected on the corresponding physical brackets for the convenience of maintenance. To verify the feasibility of the proposed method, experiments are carried out on a full-scale mock-up of C919 aircraft main landing gear cabin. The experimental results indicate that the inspection accuracy is up to 97.1%. Finally, the system has been deployed in the real C919 aircraft final-assembly workshop. The preliminary evaluation reveals that the proposed real-time AR-assisted intelligent inspection approach is effective and promising for large-scale industrial applications.

关键词： Augmented reality Deep learning Aeronautics intelligent manufacturing Assembly inspection

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Creation of a fusion image obtained in different electromagnetic ranges used in industrial robotic systems

Creation of a fusion image obtained in different electromagn...

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2020 intelligent robotics and industrial applications using computer vision Conference, iriacv 2020

作者： Semenishchev, Evgenii Voronin, Viacheslav Zelensky, Aleksandr Tolstova, Irina Moscow Russia Rostov-on-Don Russia

Modern robotic systems allow you to automate processes and increase employee productivity. To create such systems, finite state machines (sensor systems) and machine vision systems are used. The scope of their application may be the development of a robotic system within the framework of the INDUSTRY 4.0 project. The article proposes an approach based on combining data obtained by fusion from cameras working in various electromagnetic ranges. An approach is proposed that is based on a fusion of data on the shape, boundaries, and parameters of objects. The search for the boundaries and shape of objects is based on the layer-bylayer simplification of images with the allocation of local features at each level. The search for local features is based on the allocation of local stationary sections, the allocation of the boundaries of objects, the determination of their parameters and the combination of information in a single information space. The search for boundaries is based on the use of the method of combined image analysis with a joint analysis of the L2 norm criterion. As a measure of a discrepancy, the square of the difference of the input value and the resulting estimate is used. As an example, the results of fusing images based on the combination of infrared data, RGB data, and IR cameras are presented. © 2020 Society for Imaging Science and Technology.

关键词： Cameras

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2D label free microscopy imaging analysis using machine learning

2D label free microscopy imaging analysis using machine lear...

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2020 intelligent robotics and industrial applications using computer vision Conference, iriacv 2020

作者： Hu, Han Lei, Yang Xin, Daisy Shkolnikov, Viktor Barcelo, Steven Allebach, Jan Delp, Edward J. School of Electrical and Computer Engineering Purdue University West LafayetteIN United States HP Labs HP Inc. Palo AltoCA United States

Separation and isolation of living cells plays an important role in the fields of medicine and biology with label-free imaging often used for isolating cells. The analysis of label-free cell images has many challenges when examining the behavior of cells. This paper presents methods to analyze label-free cells. Many of the tools we describe are based on machine learning approaches. We also investigate ways of augmenting limited availability of training data. Our results demonstrate that our proposed methods are capable of successfully segmenting and classifying label-free cells. © 2020, Society for Imaging Science and Technology.

关键词： Cells

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Head based tracking

Head based tracking

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2020 intelligent robotics and industrial applications using computer vision Conference, iriacv 2020

作者： Ullah, Mohib Mahmud, Maqsood Ullah, Habib Ahmad, Kashif Imran, Ali Shariq Cheikh, Faouzi Alaya Norwegian University of Science and Technology Norway Department of Management Information System University of Imam Abdulrahman Bin Faisal Dammam Saudi Arabia College of Computer Science and Engineering University of Ha'il Saudi Arabia Doha Qatar

For tracking multiple targets in a scene, the most common approach is to represent the target in a bounding box and track the whole box as a single entity. However, in the case of humans, the body goes through complex articulation and occlusion that severely deteriorate the tracking performance. In this paper, we argue that instead of tracking the whole body of a target, if we focus on a relatively rigid body organ, better tracking results can be achieved. Based on this assumption, we followed the tracking-by-detection paradigm and generated the target hypothesis of only the spatial locations of heads in every frame. After the localization of head location, a constant velocity motion model is used for the temporal evolution of the targets in the visual scene. For associating the targets in the consecutive frames, combinatorial optimization is used that associates the corresponding targets in a greedy fashion. Qualitative results are evaluated on four challenging video surveillance dataset and promising results has been achieved. © 2020 Society for Imaging Science and Technology. All rights reserved.

关键词： Combinatorial optimization

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