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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Lokoč, Jakub Vopálková, Zuzana Stroh, Michael Buchmueller, Raphael Schlegel, Udo SIRET Research Group Department of Software Engineering Faculty of Mathematics and Physics Charles University Prague Czech Republic Visual Computing Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany Data Analysis and Visualization Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany

ISBN: (纸本)9783031533013

This paper presents a tool relying on data service architecture, where technical details of all VBS datasets are completely hidden behind an abstract stateless data layer. The data services allow independent development of interactive search interfaces and refinement techniques, which is demonstrated by a smart front-end component. The component supports common search features and allows users to exploit content-based statistics for effective filtering. We believe that video data services might be a valuable addition to the open-source VBS toolkit, especially when available for the competition on a shared server with all VBS datasets, extracted features, and meta-data behind. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Video recording

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Sensor design and integration into small sized autonomous vehicle 2

Sensor design and integration into small sized autonomous ve...

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2nd IEEE Conference on Information Technology and data science, CITDS 2022

作者： Kovacs, Laszlo Baranyai, David Giraszi, Tamas Majoros, Tamas Kovacs, Adam Vagner, Mate Palkovics, Denes Berczes, Tamas University of Debrecen Nvidia Deep Learning Institute Certified Instructor Department of Data Science and Visualization Faculty of Informatics Debrecen Hungary University of Debrecen Department of Data Science and Visualization Faculty of Informatics Debrecen Hungary University of Debrecen Faculty of Informatics Debrecen Hungary University of Debrecen Department of It Systems and Networks Faculty of Informatics Debrecen Hungary

ISBN: (数字)9781665496537

ISBN: (纸本)9781665496537

Autonomous vehicles use several different kinds of sensors to get information about the surrounding area. With sensors and artificial intelligence, the autonomous vehicle tries to find the optimal decision as close as possible to the appropriate behavior. Because of the huge amount of data, the usage of modern machine learning and data-driven approaches is necessary. Although computing big data is not easily handled especially onboard a vehicle, the critical mass of the diverse data generated from different sources is essential. In the field of autonomous vehicles, there have not been standards yet, but the range of applied sensors is well-known. Most systems use a combination of cameras, radar, and LIDAR (Light Detection and Ranging) sensors that transmit data to a central computer that detects the environment around the car. Self-driving development could be supported with model-sized self-driving vehicles because of the complexity of the area. The development of autonomous vehicles consists of security, communication, and data processing issues. Mistakes are increasing the risks of potential accidents. The realistic environment which can be simulated or built makes it possible that the learned behavior can be carried across the platforms while the differences in the sizes are not playing an important role in the matter of learning. The previous reason causes the model-size self-driving development to be more cost-effective. In our work, we developed a self-driving model car with different types of sensors. Measurement data from them can be used to improve the self-driving capabilities of the vehicle. © 2022 IEEE.

关键词： Autonomous vehicles

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Deep learning-based anomaly detection for imaging in autonomous vehicles 2

Deep learning-based anomaly detection for imaging in autonom...

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2nd IEEE Conference on Information Technology and data science, CITDS 2022

作者： Kapusi, Tibor Peter Kovacs, Laszlo Hajdu, Andras University of Debrecen Faculty of Informatics Department of Data Science and Visualization Debrecen Hungary Nvidia Deep Learning Institute Certified Instructor Hungary

ISBN: (数字)9781665496537

ISBN: (纸本)9781665496537

Autonomous driving and self-driven vehicles have become among the most pursued research areas in recent years. Nowadays, various driving tasks can be solved by applying the newest machine learning techniques such as line tracking, traffic sign recognition, automated speed adjustment, and parking. However, difficult visual conditions and anomalies can cause problems in selected algorithms, which may occur unexcepted and failure operations in these cases. It is also expected not just very expensive to do such kinds of experiments, but these problematic conditions are also lead to dangerous traffic situations at the same time. We made an effort to put these kinds of studies into a cost-effective and safe model-scale environment. This paper introduces an anomaly detection method capable of recognizing abnormal and burnt-out objects in image scenes. Our proposed method is based on a fast neural network architecture using YOLO layers to detect regions. Our experiments demonstrate the capabilities and detection accuracy of the designed neural network, called anomalyNet, with the complete training and evaluation process. In the study, we work with publicly available datasets, but our model-sized track and DAVE (University of Debrecen Autonomous VehiclE) play an important role also. © 2022 IEEE.

关键词： Anomaly detection

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Fast, GPU-based Computation of Large Point-Spread Function Sets for the Human Eye using the Extended Nijboer-Zernike Approach 2

Fast, GPU-based Computation of Large Point-Spread Function S...

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2nd IEEE Conference on Information Technology and data science, CITDS 2022

作者： Csoba, Istvan Kunkli, Roland University of Debrecen Faculty of Informatics Doctoral School of Informatics Debrecen Hungary University of Debrecen Faculty of Informatics Department of Data Science and Visualization Debrecen Hungary

ISBN: (数字)9781665496537

ISBN: (纸本)9781665496537

The point-spread function (PSF) is the diffraction pattern of an infinitesimal light source and plays an important role in the study and simulation of human vision. It forms the backbone of a multitude of vision-rendering algorithms, as it can be used to obtain the necessary kernels for convolution. Its computation is often performed via ray-tracing or the fast Fourier transform (FFT), but recently we also demonstrated that the Extended Nijboer-Zernike (ENZ) approach can be a more efficient alternative, which reduces the computation time of large PSF sets to just a few minutes. In this paper, we present a significantly faster, GPU-based computation scheme of the ENZ approach to further improve the computation process for such large PSF sets. Our algorithm works by reformulating the core V-{n}-{m} function to reusable subterms that are efficient to accumulate in parallel. We demonstrate that our proposed method leads to substantial performance improvements and facilitates the interactive exploration of visual aberrations when paired with our existing vision simulation algorithm. © 2022 IEEE.

关键词： Optical transfer function

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Utilizing Computer Vision for Beef Cattle Feeding Bunk Score Assessment with a Calan Gate Feeding System 11

Utilizing Computer Vision for Beef Cattle Feeding Bunk Score...

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11th European Conference on Precision Livestock Farming

作者： Mendes, Egleu D.M. Wooley, Jack Pi, Yalong Tao, Jian Tedeschi, Luis O. Department of Animal Science Texas A&M University College StationTX United States Institute of Data Science Texas A&M University College StationTX United States School of Performance Visualization & Fine Arts Texas A&M University College StationTX United States

ISBN: (纸本)9791221067361

Our study employed a novel methodology to assess the effectiveness of a CV model in classifying feed leftovers (orts) within a Calan Gate Feeding System. We used six bunk scores: S00 (no feed), S05 (scattered feed), S10 (thin layer), S20 (25-50%), S30 (> 50%), and S40 (untouched). With the use of three RGB (red green blue) cameras, we captured a dataset of 12 156 images, which were then segmented into training (70%), validation (15%), and testing (15%) sets. The analysis with ResNet revealed high initial model performance, with precision, recall, and F1 scores reaching 0.9989 and testing accuracy of 99.89%. However, when applied to an independent dataset of 7 905 images, the performance dropped, notably for precision (0.5868), recall (0.3982), and F1 score (0.3653). These discrepancies likely resulted from varying lighting conditions, the mismatch between typical open-feeding bunk classifications and the Calan Gate's closed design, and model overfit. To optimize the effectiveness of the feeding bunk scoring system, we suggest refining the classification into three tailored categories: C1) empty bunk (S00), C2) scattered or thin layer (S05 + S10), and C3) more than 25% to full (S20, S30, S40). This integration of CV into PLF can automate the feeding scores to estimate individual consumption accurately. Such advancements can improve resource utilization, optimize animal performance, and reduce operational costs, thereby contributing to cost-effective and sustainable production methods. This study underscores the necessity of continuous model training and adaptation, a crucial aspect that ensures reliable CV application in real-world settings and is of utmost importance in your research. © 2024 11th European Conference on Precision Livestock Farming. All rights reserved.

关键词： Precision agriculture

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419 Automated individual animal identification and feeding bunk scoring: a computer vision approach for beef cattle at Calan gate feeding system

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Journal of Animal science 2024年第SUPPLEMENT_3期102卷 2-3页

作者： Mendes, Egleu D M Wooley, Jack Pi, Yalong Tao, Jian Tedeschi, Luis O Department of Animal Science Texas A&M University Institute of Data Science Texas A&M University School of Performance Visualization & Fine Arts Texas A&M University

Recent developments in computer vision (CV) technology have significantly improved the management of beef cattle feeding systems by enabling precise monitoring and adjustment of feed intake based on individual needs. This study introduces an automated approach for identifying cattle at feeding bunks using CV and evaluates the effectiveness of a feeding bunk scoring system to optimize cattle feeding strategies. Utilizing a high-definition video capture setup, our research focused on the feeding behaviors of 6 heifers in one pen with the Calan Feeding System (American Calan, Northwood, NH). We deployed three Reolink PoE cameras (Model D400) strategically positioned above the feeding bunks, each overseeing two feeding bunks, to monitor feeding activity comprehensively. The cameras were set to record in Full HD (1920 x 1080 pixels) at 30 frames per second. We processed the frames using Python’s OpenCV, resulting in 600 images that composed an array with dimensions 600 x 400x450 (pixels) x 3 (RGB). These images served as the basis for our analysis, which involved feature extraction via a ResNet-50 convolutional neural network, followed by dimensionality reduction through Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE). Our methodology included the use of k-means clustering to detect the presence of cattle at the bunk, achieving an Adjusted Rand Index (ARI) of 0.9933. We applied the DBSCAN clustering algorithm in the ‘cattle present’ images for individual animal identification, obtaining an ARI of 0.9174, indicating high model accuracy. To assess feeding efficiency, we analyzed 12,156 images of feeding bunks obtained using the same Python’s OpenCV approach and then classified them into six categories based on Lundy et al., 2015: S00 (no feed), S05 (scattered feed), S10 (thin layer), S20 (25 to 50%), S30 (>50%), and S40 (untouched). Using a training, validation, and testing split of 70%, 15%, and 15%, respectively, our model demon

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3D Localization and data Quality Estimation with Marvelmind 2

3D Localization and Data Quality Estimation with Marvelmind

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2nd IEEE Conference on Information Technology and data science, CITDS 2022

作者： Vagner, Mate Palkovics, Denes Kovacs, Laszlo University of Debrecen Faculty of Informatics Debrecen Hungary Nvidia Deep Learning Institute Certified Instructor University of Debrecen Faculty of Informatics Department of Data Science and Visualization Debrecen Hungary

ISBN: (数字)9781665496537

ISBN: (纸本)9781665496537

Localization and position estimation are crucial tasks in autonomous driving. In addition to the importance of positioning, with good quality position tracking, it is possible to implement sophisticated data collection procedures and use advanced machine learning methods such as reinforced learning. Global navigation satellite systems offer very accurate positioning, but their use is cumbersome or impossible under certain laboratory conditions. In our work, we applied an indoor positioning system, that is integrated into our 1:16 self-driving car. © 2022 IEEE.

关键词： Indoor positioning systems

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ML-Based Online Traffic Classification for SDNs 2

ML-Based Online Traffic Classification for SDNs

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2nd IEEE Conference on Information Technology and data science, CITDS 2022

作者： Nsaif, Mohammed Kovasznai, Gergely Abboosh, Mohammed Malik, Ali Frein, Ruairi De University of Debrecen Department of Information Technology Hungary Eszterházy Károly Catholic University Department of Computational Science Hungary University of Debrecen Department of Data Science and Visualization Hungary Technological University Dublin School of Electrical and Electronic Engineering Ireland

ISBN: (数字)9781665496537

ISBN: (纸本)9781665496537

Traffic classification is a crucial aspect for Software-Defined Networking functionalities. This paper is a part of an on-going project aiming at optimizing power consumption in the environment of software-defined datacenter networks. We have developed a novel routing strategy that can blindly balance between the power consumption and the quality of service for the incoming traffic flows. In this paper, we demonstrate how to classify the network traffic flows so that the quality of service of each flow-class can be guaranteed efficiently. This is achieved by creating a dataset that encompasses different types of network traffic such as video, VoIP, game and ICMP. The performance of a number of Machine Learning techniques is compared and the results are reported. As part of future work, we will incorporate classification into the power consumption model towards achieving further advances in this research area. © 2022 IEEE.

关键词： Quality of service

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Testing MPT-GRE Multipath Solution in Vehicular Network V2I Communication 2

Testing MPT-GRE Multipath Solution in Vehicular Network V2I ...

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2nd IEEE Conference on Information Technology and data science, CITDS 2022

作者： Szilagyi, Szabolcs Kovacs, Laszlo University of Debrecen Faculty of Informatics Department of It Systems and Networks Debrecen Hungary University of Debrecen Faculty of Informatics Nvidia Deep Learning Institute Certified Instructor Department of Data Science and Visualization Debrecen Hungary

ISBN: (数字)9781665496537

ISBN: (纸本)9781665496537

A vehicular network is a communication system comprising of vehicles equipped with radio-interfaces, where the endpoints are capable of exchanging data and communication between each other (Vehicle-to-Vehicle, V2V), as well as with another mobile network or fixed infrastructure (Vehicle-to-Infrastructure, V2I). The numerous applications used in vehicles typically require seamless and increasingly fast and reliable network connections, which poses a challenge for the wireless network technologies at our disposal today. MPT-GRE, developed at the University of Debrecen, is a multi-interface access technology, which could offer a novel solution to satisfy the requirements of the services used in vehicular networking applications. Given that MPT-GRE enables the simultaneous usage of multiple network interfaces and IP-routes for vehicles, it promises to be an effective solution for vehicular networks. In this paper, we are examining the efficiency of MPT-GRE using a self-driving car model in a dual-interface Wi-Fi environment. © 2022 IEEE.

关键词： Model automobiles

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Building a dynamic opinion dashboard to categorize tweets for real-time sentiment analysis

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International Journal of Information Technology (Singapore) 2025年 1-7页

作者： Adekoya-Cole, Temitope Fernando, Sandra Maroju, Abhilash Samal, Chandra Kanta Aggarwal, Saurabh Brahma, Biswajit Assistive Technology Research Group SCDM London Metropolitan University London United Kingdom Department of Information Technology University of the Cumberlands Williamsburg United States Department of Computer Science Acharya Narendra Dev College Delhi University Delhi India San Jose State University San Jose United States Senior Data & Visualization Engineer McKesson Corporation 32559 Lake Bridgeport St Fremont United States

Twitter, a popular social media, and networking tool has evolved into an important source of real-time news and a hub for public opinions. Using Twitter data, the given paper investigates the possibility of applying sentiment analysis to identify tweets as positive, negative, or neutral and then presenting the results visually, resulting in an in-depth analysis of public opinion. To do this, a dynamic opinion dashboard is designed comprising three modules viz. the user interface built on HTML, the backend application running in Python and Twitter, and the data source. The dashboard is built on a Python Flask app that interacts with a Twitter API to collect tweets, a sentiment analysis library to classify the tweets, and a variety of visualization libraries to create charts and graphs. The proposed dashboard provides users with real-time insights from text data in a user-friendly way. The proposed work is validated and compared with existing studies as well as pre-trained sentiment analysis models based on evaluation metrics such as accuracy, precision, recall and F1 score. © Crown 2025.

关键词： data visualization Dynamic dashboard Sentiment analysis TweetNLP

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