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Stereo Analysis Extension Based on BRDF Reciprocity

Stereo Analysis Extension Based on BRDF Reciprocity

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International conference on Electronics, Communications and computers (CONIELECOMP)

作者： A. Morales-Reyes M. Arias-Estrada Optics and Electronics Computer Science Department National Institute for Astrophysics Optics and Electronics Tonantzintla Puebla Mexico

this paper presents an extension of the binocular technique which considers reciprocal reflectance of surfaces. this particular property allows to recover depth information for surfaces where others stereo techniques failed, for example, textureless and highly specular surfaces. the consideration of an arbitrary BRDF (Bidirectional Reflectance Distribution Function) improves conventional stereo algorithms, in such a way that explicit or implicit assumptions about a reflectance function are not necessary and dense depth maps are obtained. Fixed specularities and correspondence between half-occluded regions reduce processing complexity which is an important advantage of this methodology. this paper provides a general geometry assembly to this approach extending its applicability to some specific areas where accurate depth measures are required. Several results from our implementation are shown using rendered ray-tracing stereo images.

关键词： Reflectivity Surface texture Stereo vision Lighting Rendering (computer graphics) Ray tracing intelligent robots Quality control Biomedical imaging Layout

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Hy-Tech Shopping Cart Using Object Detection and Human Following Technology

Hy-Tech Shopping Cart Using Object Detection and Human Follo...

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International conference on ICT in Business Industry & Government (ICTBIG)

作者： Anita Joshi Tanishq thuse Prashant thorve Raj thakur Anshul thote Isha thakur Department Of Engineering Science and Humanities Vishwakarma Institute Of Technology Pune India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Recent advancements in artificial intelligence and robotics have transformed the retail industry, leading to innovative solutions that enhance the shopping experience. Our paper presents the High-Tech Shopping Cart, an intelligent system that leverages object detection and human-following technology to provide a seamless, hands-free shopping experience. the cart utilizes state-of-the-art object detection algorithms to identify and track items selected by shoppers while employing a robust human-following mechanism to navigate autonomously through stores. Our system integrates computer vision techniques, sensor fusion, and advanced path-planning algorithms within a modular framework, featuring cameras and depth sensors for real-time processing. Extensive experiments demonstrate high accuracy in object detection and reliable navigation in complex environments. User satisfaction surveys indicate a positive reception, highlighting the cart's potential to significantly enhance the shopping experience.

关键词： Surveys Navigation Service robots Object detection Sensor fusion Robot sensing systems Robustness Real-time systems Object recognition intelligent systems

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Pedestrian Crossing Intent Prediction Using vision Transformers

Pedestrian Crossing Intent Prediction Using Vision Transform...

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International conference on intelligent Transportation

作者： Ahmed Elgazwy Somayya Elmoghazy Khalid Elgazzar Alaa Khamis IoT Research Laboratory Ontario Tech University Oshawa Ontario Canada Connected Vehicles and HMI Group Canadian Technical Centre General Motors

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

the prediction of pedestrian intentions is crucial and one of the most challenging problems for self-driving vehicles. For this reason, a fast, efficient, and robust vision-based model is required to predict pedestrian crossing as fast as possible and to prevent serious injuries or casualties that may occur. Transformers have rapidly replaced recurrent neural networks (RNN) based architectures for their better generalization and fast performance. vision transformer (ViT) is a variant of transformers that has also proven to be efficient in image classification and has outperformed the state-of-the-art convolutional neural networks (CNN) when trained on large datasets. In this paper, a fully transformer-based architecture is presented to efficiently predict pedestrian intention with minimum latency. the proposed architecture is composed of two branches: the first branch handles the non-visual features while the second branch handles the visual features. the model is trained on the Joint Attention in Autonomous Driving (JAAD) dataset and different variants of the architecture are tested to find the optimal model. Experimental analysis shows that the proposed model outperforms all the previous state-of-the-art techniques, achieving the highest accuracy (83 %) and F1 score (64 %) on the testing dataset while maintaining the lowest processing time.

关键词： Visualization computer vision Pedestrians Accuracy Recurrent neural networks Predictive models Transformers Feature extraction Convolutional neural networks Testing

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VC-Gaussian: vision-Centric Gaussian Splatting for Dynamic Autonomous Driving Scenes

VC-Gaussian: Vision-Centric Gaussian Splatting for Dynamic A...

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International conference on intelligent Transportation

作者： Jiusi Li Tuopu Wen Kun Jiang Jinyu Miao Yining Shi Xuhe Zhao Zhigang Fan Diange Yang School of Vehicle and Mobility Tsinghua University Beijing China Zongmu Technology

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Reconstructing dynamic traffic scenes has a wide range of applications in the development of modern autonomous driving systems. Recently, novel-view synthesis techniques, such as Neural Radiance Field (NeRF) and 3D Gaussian Splatting (3D GS), have emerged as promising paradigms for the re-construction of 3D scenes. However, previous works in this area highly rely on LiDAR to provide accurate geometric prior and motion cues across frames to reconstruct dynamic objects, hindering the use of overwhelming vision data from mass-production vehicles. In this paper, we propose a novel vision-Centric reconstruction framework based on 3D GS, VC-Gaussian, which allows high-quality novel-view synthesis and dynamic scene reconstruction for autonomous driving. A composite Gaussian model is designed to represent background and foreground objects separately. To facilitate the initialization and optimization process of Gaussians without LiDAR, we leverage easy-to-obtain monocular geometric prior including metric depth and normal. Experimental results on the real autonomous driving dataset demonstrate that our method out-performs other reconstruction methods with monocular vision inputs and even is competitive with LiDAR-based methods.

关键词： Measurement Solid modeling three-dimensional displays Laser radar Dynamics Reconstruction algorithms Neural radiance field Vehicle dynamics Image reconstruction Autonomous vehicles

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Leveraging Bayesian Optimization to Enhance Self-Supervised Federated Learning of Monocular Depth Estimators

Leveraging Bayesian Optimization to Enhance Self-Supervised ...

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International conference on intelligent Transportation

作者： Elton F. De S. Soares Emilio Vital Brazil Carlos Alberto V. Campos Brazilian Research Lab (BRL) IBM Research and the Federal University of State of Rio de Janeiro (UNIRIO) Rio de Janeiro Brazil BRL UNIRIO

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Current investigations in computer vision on intelligent transportation systems have notably centered on depth estimation from images, primarily because of its cost-efficiency and diverse range of applications. Monocular methods for estimating depth have garnered significant interest due to their dependence on a single camera, which provides considerable versatility compared to binocular techniques necessitating two fixed cameras. Despite leveraging sophisticated self-supervised deep neural network learning techniques with surrogate tasks such as pose estimation and semantic segmentation, essential requirements for the practical deployment of autonomous vehicles are often neglected. these entail safeguarding data privacy, minimizing network usage, distributing computational expenses, and fortifying resilience against connectivity challenges. Recent investigations underscore the efficacy of combining federated learning with Bayesian optimization to meet these criteria without compromising the model's effectiveness. therefore, we present BOFedSCDepth, an innovative approach that combines Bayesian optimization, federated learning, and deep self-supervision to train monocular depth estimators with improved effectiveness and efficiency compared to the current state-of-the-art method in self-supervised federated learning. Evaluation experiments conducted on the KITTI dataset showcase the superiority of our method, achieving a communication cost reduction of up to 33.3% and linear computational costs at the central server with no extra burden on autonomous vehicles.

关键词： Data privacy computer vision Costs Federated learning Computational modeling Depth measurement Bayes methods Optimization Autonomous vehicles intelligent transportation systems

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EFormer-VPR: Fusing Events and Frames with Transformer for Visual Place Recognition

EFormer-VPR: Fusing Events and Frames with Transformer for V...

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International conference on intelligent Transportation

作者： Yangjing Zhang Yanqing Shen Ziyu Zhu Renwei Hai Shitao Chen Nanning Zheng National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications and Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an Shaanxi P.R. China

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Visual place recognition (VPR) is a challenging task faced by mobile robots and autonomous driving systems. In scenarios with glare or high-speed motion, image blurring makes it difficult for traditional cameras to perform reliable and accurate place recognition. In contrast, event cameras can capture target motion information without blur in high-speed motion scenes, but lack texture information in low-speed motion scenes. To leverage the complementary characteristics of these two sensors and improve the performance and robustness of VPR algorithms, we propose EFormer- VPR, which fuses target motion events and frames with transformer. this method firstly preprocesses the events stream within an adaptive time window using a clustering method, then uses transformer-based networks to extract features from motion frames and events separately and fuse them through a scoring module. Finally, features are aggregated using a VLAD layer and the whole pipeline is supervised by a triplet ranking loss. To verify the effectiveness of the proposed algorithm, we compare it with other VPR methods on the event-based driving datasets (Brisbane-Event-VPR, NeuroGPR) with challenging scenarios. Experimental results show that on the Brisbane- Event- Vprand NeuroGPR datasets, our method achieves state-of-the-art performance.

关键词： Image recognition Fuses Clustering methods Robot vision systems Clustering algorithms Feature extraction Transformers Cameras Data mining Visual place recognition

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computer vision-Based Method for Digital Twin Modelling in Railway

Computer Vision-Based Method for Digital Twin Modelling in R...

引用

International conference on intelligent Transportation

作者： Shirui Zhang Briac Leromancer Claire Nicodeme École des Ponts ParisTech (ENPC) and SNCF Research and Technology Department SNCF SA Saint Denis FRANCE Mines Paris-PSL University and SNCF

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Efficient maintenance requires knowledge and expertise about the equipment. this is especially true for public transportation and railway, where security is one of the most important pillars. However, trains and related systems are complex, making expertise difficult to acquire. In addition, given the long lifecycle of railway equipment and infrastructures, some may no longer be documented. Yet, retrieving essential information with the required details is needed. To provide the necessary data, the authors present a novel digital twin-based concept for advanced real-world information gathering. Different modelling techniques such as LiDAR Scanning, RGB-D Scanning and Deep Learning-based scene reconstruction are investigated to create texture-aware 3D models of different railway objects. Representation quality, scale adaptation and access to technologies are discussed. From this perspective, these models are promising for object recognition, predictive maintenance, extended reality (XR) tools development and agents training in intelligent Transportation Systems (ITS).

关键词： Training Adaptation models Solid modeling three-dimensional displays Computational modeling Rail transportation Digital twins Security intelligent transportation systems Public transportation

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Digital Twin-Based Office Equipment Management and Personnel Detection System

Digital Twin-Based Office Equipment Management and Personnel...

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International conference on computer Supported Cooperative Work in Design

作者： Yunpeng Gao Tinglong Tang Shuifa Sun Yirong Wu Peng Wang Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges University Yichang P.R. China Yichang Key Laboratory of Intelligent Medicine China Three Gorges University Yichang P.R. China College of Computer and Information Technology China Three Gorges University Yichang P.R. China ChangJiang YiChang Communications Administration Yichang P.R. China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In traditional office management, it is labor-intensive to perform real-time oversight on equipment and personnel. To address this challenge, this paper proposes a digital twin-based office management system. the system leverages the ESP8266 wireless module for device control and data collection, and employs the YOLOv5 deep learning model for real-time detection of employees’ working conditions. Additionally, a virtual office environment is constructed using the Unity engine. the system implemented herein enables real-time monitoring and analysis of office utilization, and assists managers in optimally allocating resources to enhance resource utilization efficiency by leveraging intelligent sensing and decision-making technologies. the system incurs low hardware and software costs, minimal data transmission latency, and rapid response times across its modules. Moreover, through data masking techniques, the system can protect the privacy of office personnel while enabling real-time monitoring.

关键词： YOLO Wireless communication Wireless sensor networks Real-time systems Software Remote working Sensors

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Comparative Analysis of Reinforcement Learning techniques in Simulated Robotics Environments

Comparative Analysis of Reinforcement Learning Techniques in...

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IEEE International conference of intelligent Robotic and Control Engineering (IRCE)

作者： Abey Yoseph M. Hassan Tanveer Razvan Cristian Voicu M. Farrukh Shahid Department of Robotics & Mechatronics Engineering Kennesaw State University Marietta Department of AI and Data Science National University of Computer and Emerging Sciences Karachi Pakistan

ISBN: (数字)9798350352399

ISBN: (纸本)9798350352405

Machine learning is a critical component of artificial intelligence and computer science that uses data and algorithms to emulate the way that humans learn and predict. Reinforcement learning, one of the many forms of machine learning, trains an agent through trial and error, with successful outcomes being rewarded and unsuccessful outcomes being penalized. the implementation of three reinforcement learning algorithms, Qlearning, Deep Q-Learning, and Double Deep Q-Learning, was tested using a series of ROS (Robot Operating System) packages in the Gazebo simulation environment. the Python openai_ros package and Gym libraries were used to input a robot into a configurable environment and test out the performance of a specific reinforcement learning algorithm. the agent’s action space was discretized into three specific actions, move forward, turn left, or turn right, that the robot could take. the reward or punishment was given after each step based on if the current action moved the robot towards the goal state or away from it. For each algorithm, the agent was first pre-trained on an obstacle free environment for 200 episodes and then was placed in an environment with two box-shaped obstacles to avoid. the algorithm/neural network parameters from the pre-training environment were loaded prior to the agent being tested in the new environment. the goal state at each episode was randomly generated and the robot’s performance was evaluated using the cumulative reward achieved per episode and the number of times it reached the goal.

关键词： Training Measurement Q-learning Machine learning algorithms Operating systems Robot vision systems Turning Prediction algorithms Libraries robots

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the Design of IR System for PC Systems

The Design of IR System for PC Systems

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Advance Computing and Innovative Technologies in Engineering (ICACITE), International conference on

作者： Jason Gunjan Chhabra Mohammed Al Sailh France Laith H. Jasim Abdul Redha Hussein Sabr Doaa Talib Zaidan Department of computer applications Hindustan institute of technology and science Department of CSE Graphic Era Hill University Adjunct Professor Graphic Era Deemed to be University Dehradun Uttarakhand College of Technical Engineering National University of Science and Technology Tareq Hafdhi Abdtawfeeq Dhi Qar Iraq Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq Computer Engineer Department Al-kunooze University College Basra Iraq Department of Laser and Optical Electronics Engineering Kut University College Al Kut Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Collaborative robotics, in conjunction with artificial intelligence (AI), offers a contemporary and effective paradigm for secure machine-human interactions. this synergy branches out into a variety of industries, including education and entertainment, in addition to industrial uses. Two intriguing board games that offer a platform for examining the potential of cooperative robotic systems are chess and checkers. An intelligent and cooperative robotic system designed for use in Italian checkers games is described in this context by the study being presented. To record the game state, the gadget employs a camera. To physically move pieces across the board, a pick-and-place mechanism is used. An algorithm is used to automatically choose the optimal moves that comply with the rules. the system respects the kinematic restrictions of the manipulator while optimizing minimum-time trajectories live for every manipulation, guaranteeing a smooth and dynamic gaming experience. An experimental validation employing a seven-degree-of-freedom Franka Emika arm verifies the effectiveness of the proposed approach in real-world settings.

关键词： Service robots Heuristic algorithms Robot vision systems Collaboration Entertainment industry Games Kinematics

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