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IEEE International Conference on Electro-Information Technology

作者： Mikhail Gromov David Arnold Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL U.S.A.

computer vision has proven itself capable of accurately detecting and classifying objects within images. This also works in cases where images are used as a way of representing data, without being actual photographs. In cybersecurity, computer vision is rarely used, however it has been used to detect botnets successfully. We applied computer vision to determine how well it would be able to detect and classify a large number of attacks and determined that it would be able to run at a decent rate on a Jetson Nano. This was accomplished by training a convolutional neural network using data publicly available in the IoT-23 database, which contains packet captures of IoT devices with and without different malware infections. The neural network was evaluated on an RTX 3050 and a Jetson Nano to see if it could be used in IoT.

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Drowning Recognition for Ocean Surveillance using computer vision and Drone Control

Drowning Recognition for Ocean Surveillance using Computer V...

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IEEE International Conference on Electro-Information Technology

作者： Maureen Rakotondraibe Tianyang Fang Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL U.S.A.

According to WHO's report from 2021, Drowning is the 3rd leading cause of unintentional death worldwide. The use of autonomous drones for drowning recognition can increase the survival rate and help lifeguards and rescuers with their life saving mission. This paper presents a real-time drowning recognition model and algorithm for ocean surveillance that can be implemented on a drone. The presented model has been trained using two different approaches and has 88% accuracy. Compared to the contemporary models of drowning recognition designed for swimming pools, the model presented is better suited for outdoor applications in the ocean.

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NTIRE 2023 HR NonHomogeneous Dehazing Challenge Report

NTIRE 2023 HR NonHomogeneous Dehazing Challenge Report

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2023 IEEE/CVF Conference on computer vision and Pattern Recognition Workshops, CVPRW 2023

作者： Ancuti, Codruta O. Ancuti, Cosmin Vasluianu, Florin-Alexandru Timofte, Radu Zhou, Han Dong, Wei Liu, Yangyi Chen, Jun Liu, Huan Li, Liangyan Wu, Zijun Dong, Yubo Li, Yuyan Qiu, Tian He, Yu Lu, Yonghong Wu, Yinwei Jiang, Zhenxiang Liu, Songhua Yang, Xingyi Jing, Yongcheng Benjdira, Bilel Ali, Anas M. Koubaa, Anis Yang, Hao-Hsiang Chen, I-Hsiang Chen, Wei-Ting Huang, Zhi-Kai Chen, Yi-Chung Hsieh, Chia-Hsuan Chang, Hua-En Chiang, Yuan-Chun Kuo, Sy-Yen Guo, Yu Gao, Yuan Liu, Ryan Wen Lu, Yuxu Qu, Jingxiang He, Shengfeng Ren, Wenqi Hoang, Trung Zhang, Haichuan Yazdani, Amirsaeed Monga, Vishal Yang, Lehan Wu, Alex Jiahao Mai, Tiancheng Cong, Xiaofeng Yin, Xuemeng Yin, Xuefei Emad, Hazim Abdallah, Ahmed Yasser, Yahya Elshahat, Dalia Elbaz, Esraa Li, Zhan Kuang, Wenqing Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Zhang, Zhao Wei, Yanyan Wang, Junhu Zhao, Suiyi Zheng, Huan Guo, Jin Sun, Yangfan Liu, Tianli Hao, Dejun Jiang, Kui Sarvaiya, Anjali Prajapati, Kalpesh Patra, Ratnadeep Barik, Pragnesh Rathod, Chaitanya Upla, Kishor Raja, Kiran Ramachandra, Raghavendra Busch, Christoph ETcTI Universitatea Politehnica Timisoara Romania ICTEAM UCL Belgium Computer Vision Lab University of Wuerzburg Germany Computer Vision Lab ETH Zurich Switzerland Department of Electrical and Computer Engineering McMaster University Canada Department of Electrical and Computer Engineering University of Alberta Canada McMaster University Canada Xidian University China Research Institute Singapore National University of Singapore Singapore University of Sydney Australia Robotics and Internet-of-Things Laboratory Prince Sultan University Riyadh12435 Saudi Arabia Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Wuhan University of Technology China Singapore Management University Singapore Singapore Sun Yat-sen University China Electrical Engineering Department Pennsylvania State University United States The University of Sydney Australia Southeast University China University of California Los Angeles United States Beijing Jiaotong University China Mansoura Univeristy Egypt College of Information Science and Technology Jinan University China Department of Information Technology Uppsala University Sweden Hefei University of Technology China Zhejiang Dahua Technology China Sardar Vallabhbhai National Institute of Technology India Norwegian University of Science and Technology Norway

ISBN: (纸本)9798350302493

This study assesses the outcomes of the NTIRE 2023 Challenge on Non-Homogeneous Dehazing, wherein novel techniques were proposed and evaluated on new image dataset called HD-NH-HAZE. The HD-NH-HAZE dataset contains 50 high resolution pairs of real-life outdoor images featuring nonhomogeneous hazy images and corresponding haze-free images of the same scene. The nonhomogeneous haze was simulated using a professional setup that replicated real-world conditions of hazy scenarios. The competition had 246 participants and 17 teams submitted solutions for the final testing phase. The proposed solutions demonstrated the cutting-edge in image dehazing technology. © 2023 IEEE.

关键词： Demulsification

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IoT-Enabled Smart Bike Helmet with an AI-Driven Collision Avoidance System

IoT-Enabled Smart Bike Helmet with an AI-Driven Collision Av...

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IEEE International Conference on Electro-Information Technology

作者： Jacob Solus Maureen Rakotondraibe Xinrui Yu Won-Jae Yi Mikhail Gromov Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL U.S.A.

This paper presents a system design for a smart bike helmet with multiple safety features that are intended to empower bicycle riders to proactively avoid potential sources of danger or injury. A Smart Sensor/Actuator Node (SSAN), driven by an Arduino Uno single-board microcontroller, contains input sensors and actuators to provide riders the ability to send and receive warnings promptly on their helmet. A vision Node, driven by an NVIDIA Jetson Nano and a cable pin-connected camera, executes AI object detection algorithms for any dangerous objects that are out of sight of the rider and sends alerts to the SSAN as needed. By combining safety features of the SSAN and vision Node while continuously sending data to an IoT-enabled backend web server, the safety operation of a typical bike ride can be substantially improved.

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Low-cost Mars Terrain Classification System Based on Coarse-grained Annotation

Low-cost Mars Terrain Classification System Based on Coarse-...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Zhang, Jian Chen, Yeheng Zhu, Shiqiang Li, Yuehua Xie, Tian Gu, Jason Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9781665481090

Due to communication latency with remote ground sites, automatic recognition of Mars terrain is essential for the path-planning of rovers. Currently, most vision-based terrain classification require thousands of fine-grained training samples, while the undefined terrains on Mars are difficult to be classified or fine-grained labeled. Actually, most of the terrain categories can only be coarse-grained labeled due to several limitations, such as overlapped sub-regions, blurred borders, etc. To solve this problem, CACMT (Coarse-grained Annotation-based Classification for Mars Terrain) is proposed to generate the global fine-grained classification map from the local coarse-grained data. Specifically, the complete pipeline is decomposed into (i) annotation rules with unique design, (ii) hierarchical feature fusion network for predicting sub-features of terrain (iii) and a generator for outputting dense terrain categories of Mars. Finally, the results of actual data on Mars demonstrate that the terrain sub-features can be successfully recognized and a dense terrain classification map can be generated applying only coarse-grained labeled images. © 2022 IEEE.

关键词： Motion planning

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Robust 3D Face Alignment with Multi-Path Neural Architecture Search

Robust 3D Face Alignment with Multi-Path Neural Architecture...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Zhichao Jiang Hongsong Wang Xi Teng Baopu Li Institute of Deep Learning (IDL) Baidu Beijing China Department of Computer Science and Engineering Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Nanjing China Computer Vision Technology Institution Baidu Beijing China Baidu Research Baidu Sunnyvale USA

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

3D face alignment is a very challenging and fundamental problem in computer vision. Existing deep learning-based methods manually design different networks to regress either parameters of a 3D face model or 3D positions of face vertices. However, designing such networks relies on expert knowledge, and these methods often struggle to produce consistent results across various face poses. To address this limitation, we employ Neural Architecture Search (NAS) to automatically discover the optimal architecture for 3D face alignment. We propose a novel Multi-path One-shot Neural Architecture Search (MONAS) framework that leverages multi-scale features and contextual information to enhance face alignment across various poses. The MONAS comprises two key algorithms: Multi-path Networks Unbiased Sampling Based Training and Simulated Annealing based Multi-path One-shot Search. Experimental results on three popular benchmarks demonstrate the superior performance of the MONAS for both sparse alignment and dense alignment.

关键词： Training Learning systems Solid modeling computer vision Three-dimensional displays Neural networks Simulated annealing Network architecture Search problems Faces

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Holistic Network Virtualization and Pervasive Network Intelligence for 6G

arXiv

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arXiv 2023年

作者： Shen, Xuemin Gao, Jie Wu, Wen Li, Mushu Zhou, Conghao Zhuang, Weihua The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada The Department of Electrical and Computer Engineering Marquette University MilwaukeeWI53233 United States The Frontier Research Center Peng Cheng Laboratory Guangdong Shenzhen518055 China

In this tutorial paper, we look into the evolution and prospect of network architecture and propose a novel conceptual architecture for the 6th generation (6G) networks. The proposed architecture has two key elements, i.e., holistic network virtualization and pervasive artificial intelligence (AI). The holistic network virtualization consists of network slicing and digital twin, from the aspects of service provision and service demand, respectively, to incorporate service-centric and user-centric networking. The pervasive network intelligence integrates AI into future networks from the perspectives of networking for AI and AI for networking, respectively. Building on holistic network virtualization and pervasive network intelligence, the proposed architecture can facilitate three types of interplay, i.e., the interplay between digital twin and network slicing paradigms, between model-driven and data-driven methods for network management, and between virtualization and AI, to maximize the flexibility, scalability, adaptivity, and intelligence for 6G networks. We also identify challenges and open issues related to the proposed architecture. By providing our vision, we aim to inspire further discussions and developments on the potential architecture of 6G. © 2023, CC BY-NC-SA.

关键词： Network architecture

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Indoor Navigation System for Visually Impaired People using computer vision

Indoor Navigation System for Visually Impaired People using ...

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2022 IEEE International Conference on Electro Information Technology, eIT 2022

作者： Fraga, Adrian Leyte Yu, Xinrui Yi, Won-Jae Saniie, Jafar Illinois Institute of Technology Embedded Computing and Signal Processing Department of Electrical and Computer Engineering Research Laboratory ChicagoIL United States Universidad Politécnica de Madrid E.T.S.I. Industriales Madrid Spain

ISBN: (纸本)9781665480093

This paper studies an indoor navigation guidance system for visually impaired people using Artificial Intelligence (AI) and computer vision techniques to guide users via optimal path based on quick response (QR) code markers and collision avoidance system supported by the monocular depth estimation algorithm. The proposed system utilizes a set of QR code markers, as location beacons, to generate an optimal path for the user to reach the destination. The identified QR code markers are used to correlate and compare information from the online database containing valuable information such as current location information, and the next possible locations from the current location. In addition, our system embraces a safety feature, a collision avoidance system, by utilizing the monocular depth estimation algorithm to identify obstacles in the path to the desired destination. All obtained information is provided to the user through a text-to-speech engine, where the system can direct the user to the optimal path via audio output. © 2022 IEEE.

关键词： Location

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Exploring Seasonal Variability in the Context of Neural Radiance Fields for 3D Reconstruction on Satellite Imagery 46th

Exploring Seasonal Variability in the Context of Neural R...

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46th Annual Conference of the German Association for Pattern Recognition, DAGM-GCPR 2024

作者： Kåreborn, Liv Ingerstad, Erica Berg, Amanda Karlsson, Justus Haglund, Leif Maxar International Sweden AB Ebbegatan 13 Linköping582 13 Sweden AI Sweden Folkungagatan 44 Stockholm118 26 Sweden Computer Vision Laboratory Department of Electrical Engineering Linköping University Linköping581 83 Sweden

ISBN: (纸本)9783031851865

In this work, the seasonal predictive capabilities of Neural Radiance Fields (NeRF) applied to satellite images are investigated. Focusing on the utilization of satellite data, the study explores how Sat-NeRF, a novel approach in computer vision, performs in predicting seasonal variations across different months. Through comprehensive analysis and visualization, the study examines the model’s ability to capture and predict seasonal changes, highlighting specific challenges and strengths. Results showcase the impact of the sun direction on predictions, revealing nuanced details in seasonal transitions, such as snow cover, color accuracy, and texture representation in different landscapes. Given these results, we propose Planet-NeRF, an extension to Sat-NeRF capable of incorporating seasonal variability through a set of month embedding vectors. Comparative evaluations reveal that Planet-NeRF outperforms prior models in the case where seasonal changes are present. The extensive evaluation combined with the proposed method offers promising avenues for future research in this domain. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： 3D reconstruction

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Design Flow and Implementation of an AI-Enabled Smart Guidance System for Art Galleries

Design Flow and Implementation of an AI-Enabled Smart Guidan...

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2022 IEEE International Conference on Electro Information Technology, eIT 2022

作者： Esteban, Alvaro Belloso Delgado, Mario Silvestre Yu, Xinrui Yi, Won-Jae Saniie, Jafar Illinois Institute of Technology Embedded Computing and Signal Processing Department of Electrical and Computer Engineering Research Laboratory ChicagoIL United States Universidad Politécnica de Madrid XE.T.S.I. Industriales Madrid Spain

ISBN: (纸本)9781665480093

Audio guides are commonly utilized to enrich the experience of art gallery visitors and to fully engage them with the artwork by providing background, contexts, and other information related to the corresponding artists. However, this method may be monotonous to the public and cannot automatically change to the appropriate recording when users move to the next artwork. The traditional operation of audio guides is only enabled by the user's visual recognition of the artwork to activate appropriate commentaries manually. In this paper, we explore the potential enhancement of the art gallery visitors' experience by applying Artificial Intelligence (AI), machine learning, and computer vision techniques to recognize the artwork's main features automatically and provide the corresponding artist and other information to the visitors. We train a model using a Deep Convolutional Neural Network (CNN) and Kaggle's Painter by Numbers dataset, and host the trained model on a low-power AI accelerator equipped with a camera to recognize the artwork in real-time. The results are then transmitted to a database, which is utilized by a custom Android application on the smartphone to provide updated artworkrelated information on the display or by audio. Our system targets not only the general public for an interactive experience in the art gallery but also visually impaired people for proper guidance with the smartphone's camera and its voice feedback capabilities. © 2022 IEEE.

关键词： Deep neural networks

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