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共 17893 条记录，以下是291-300 订阅

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Improving Height Prediction for vision-Based Roadside 3D Object Detection

Improving Height Prediction for Vision-Based Roadside 3D Obj...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Zhang, Tengfei Zhang, Heng Li, Rengang Zhao, Yaqian Deng, Qi Li, Ruyang Electronic Information Industry Co. Ltd. Beijing China IEIT SYSTEMS Co. Ltd. Jinan China

ISBN: (纸本)9798350368741

Roadside vision-based 3D object detection is vital in many applications, such as autonomous driving. The mainstream methods enhance the accuracy of distance estimation by converting predicted height distribution into depth distribution. However, predicting object's height in roadside perception is challenging, particularly for distant and small objects. Therefore, this work proposes a series of methods to optimize height prediction. Firstly, we propose depth and height decoding supervision methods to optimize the height network by supervising the decoded depth and height values. Then, a height distribution alignment loss is introduced to optimize the height network by constraining the consistency of height distributions among objects of the same category. Experimental results demonstrate that the proposed optimization methods can effectively improve the accuracy of roadside vision-based 3D object detection. For instance, our methods improve the accuracies by 2.31%, 4.33%, and 4.24% for the cyclist category at easy, medium, and hard levels, respectively. © 2025 IEEE.

关键词： height prediction Object detection roadside

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Object Tracking robot Using SIFT Based Computer vision 1

Object Tracking Robot Using SIFT Based Computer Vision

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1st IEEE international conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Deepak Kamath, K. Rizvi, Dabir Hasan Adarsh, U. Karthik, B. Elumalai, R. CMR Institute of Technology Dept. of ECE Bengaluru India

ISBN: (纸本)9798350371802

Latterly, we have seen an immense increase in the amount of research, development and investment dedicated to the field of Autonomous Vehicle Technology or in common terms - Self-Driving Vehicles. While modern autonomous vehicles commonly use Machine and Deep Learning models using data from sensors to understand the environment and move autonomously, this project demonstrates the use of a digital image processing algorithm- SIFT, to enable a robot to track specific objects in an environment and follow them. The implementation of an efficient and innovative image tracking algorithm for autonomous vehicles is the most important outcome of this work. This paper mentions a few use cases of this work along with the potential and future scope of it. © 2024 IEEE.

关键词： Computer vision

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Leveraging Visual Captions for Enhanced Zero-Shot HOI Detection

Leveraging Visual Captions for Enhanced Zero-Shot HOI Detect...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Zeng, Yanqing Mao, Yunyao Lu, Zhenbo Zhou, Wengang Li, Houqiang EEIS Department University of Science and Technology of China Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

ISBN: (纸本)9798350368741

Zero-shot Human-Object Interaction (HOI) detection aims to identify both seen and unseen HOI categories in an image. Most existing methods rely on semantic knowledge distilled from CLIP to find novel interactions but fail to fully exploit the powerful generalization ability of vision-language models, leading to impaired transferability. In this paper, we introduce a novel framework for zero-shot HOI detection. We first utilize vision-language models (VLMs) to generate visual captions from multiple perspectives, including humans, objects, and environments, to enhance interaction understanding. Then, we propose a multi-modal fusion encoder to fully leverage these visual captions. Additionally, to equip the HOI detector with a thorough consideration of contextual information in the image, we design a novel multi-branch HOI network that aggregates features at the instance, union, and global levels. Experiments on prevalent benchmarks demonstrate that our model achieves promising performance under a variety of zero-shot settings. The source codes are available at https://***/aqingcv/VC-HOI. © 2025 IEEE.

关键词： Human-object Interaction Multimodal fusion vision-language Model Zero-shot

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Using Quadcopter for vision-based Tracking of High-Rise Vertical Structure 20

Using Quadcopter for Vision-based Tracking of High-Rise Vert...

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20th IEEE/ASME international conference on Mechatronic and Embedded Systems and Applications (MESA)

作者： Gupta, Ayush Shukla, Amit Sivarathri, Ashok Kumar Pandya, Naisarg Indian Inst Technol Mandi Sch Mech & Mat Engn Mandi Himachal Prades India Indian Inst Technol Mandi Ctr Artificial Intelligence & Robot Mandi Himachal Prades India

ISBN: (纸本)9798331516246;9798331516239

This research paper focuses on developing vision-based autonomous navigation and motion control algorithms for quadcopters to track high-rise industrial vertical structures. A camera, fixed on the drone will capture the Red-Green-Blue (RGB) channel image and will feed to the image processing node, which is a convolution neural network (CNN) based encoder-decoder model for image segmentation. The segmented image will go to the classical computer vision algorithm to get the desired features for navigation and the motion control algorithm. In this paper, we have implemented a nonlinear sliding mode control algorithm for motion control and discuss the results of positioning and motion control of the drone for the motion in circular and spiral paths. The entire experimentation is performed on the DJI M100 drone with the Nvidia Jetson Xavier NX as an onboard computer.

关键词： Autonomous tracking Computer vision Encoder-Decoder and sliding mode control

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Comparative Study Of Indoor Performance Metrics In Ibra City's Mobile Broadband Networks, Oman 6

Comparative Study Of Indoor Performance Metrics In Ibra City...

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6th international conference on Communications signal processing and their Applications

作者： Al Jahdhami, Majan Abdullah El-Saleh, Ayman Alhammadi, Abdulraqeb Shayea, Ibraheem ASharqiyah Univ ASU Dept Elect & Commun Engn Ibra Oman UTM Ctr Artificial Intelligence & Robot CAIRO Malaysia Japan Int Inst Technol Kuala Lumpur Malaysia ITU Fac Elect & Elect Engn Elect & Commun Engn Dept Istanbul Turkiye

ISBN: (纸本)9798350384826;9798350384819

In today's digital age, characterized by the Internet of Things (IoT), cloud computing, and emerging tools such as augmented reality and artificial intelligence, networks play a crucial role. These networks rely on fast data exchange and seamless communication. Broadband networks have become the backbone of modern communications. This paper will provide a comprehensive review, followed by a simplified comparison specific to Oman. The study also conducts a comparative evaluation of performance metrics for indoor environments within mobile broadband networks in the city of Ibra, Oman. The focus will be on parameters including latency, signal-to-noise ratio (SNR), and throughput. The research utilizes field measurements obtained through G-Net Track Pro software and employs statistical analysis to assess network quality across the three major mobile network operators (MNOs) in Oman. Finally, indoor measurements in the Sultanate of Oman are compared to previous studies conducted in Oman. The results reveal significant disparities in performance among operators, which can be attributed to differences in infrastructure, technology, and optimization strategies. The insights derived from this study are of great value to network operators and regulatory authorities, as they enable targeted improvements in service quality. Furthermore, this research contributes to the methodology for conducting similar studies in diverse contexts.

关键词： Mobile broadband mobile network operators (MNOs) Latency signal to noise ratio(SNR) throughput

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GPA: Enhancing Generalizable Physical Adversarial Attacks Across Multiple vision Tasks

GPA: Enhancing Generalizable Physical Adversarial Attacks Ac...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Xie, Mingye Xiang, Suncheng Ruan, Jiacheng Gao, Xian Yu, Zefang Liu, Ting Fu, Yuzhuo Shanghai Jiao Tong University China

ISBN: (纸本)9798350368741

Adversarial attacks pose a significant challenge in deep learning, as carefully crafted perturbations can severely degrade even the most advanced models. In real-world scenarios, where the target models are often unknown, previous works often focus on creating adversarial patterns for specific known models, with the goal of generalizing these patterns to other models. However, such attacks rely heavily on prior model information, leading to poor generalization. To overcome this, we propose a novel method called GPA. Our solution includes an attention extraction module based on a pre-trained vision encoder, which captures precise and generalizable features of model attention on objects. We also introduce attack loss functions that divert attention away from target objects. Compared to state-of-the-art methods, our approach achieves superior attack performance across various downstream vision tasks, including object detection, instance segmentation, and depth estimation. Moreover, the adversarial patterns generated by GPA maintain their effectiveness in real-world scenarios. © 2025 IEEE.

关键词： adversarial attack instance segmentation object detection physical attack

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EOG-Assist: Real-Time EOG-Based Eye Tracking for Assistive Systems 18

EOG-Assist: Real-Time EOG-Based Eye Tracking for Assistive S...

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18th international conference on Control Automation robotics and vision

作者： Narayan, Barath S. Janamanchi, Sreyas Harsha, Achintya Nitheezkant, R. Jonna, Prashanth Rao, Madhav IIIT Bangalore Surg & Assist Robot Lab Bangalore Karnataka India

ISBN: (纸本)9798331518509;9798331518493

Paralysed patients find it hard to move various body parts depending on the severity of the injury. In an attempt to recover the motor imagery skills, rehabilitation schemes are recommended. One of the biggest barrier is the continuously dropping motivation for performing physiotherapy, besides the logistics hardships constantly borne by the care-taker. While the latter challenge remains personal, various Human-Computer-Interaction (HCI) emerged applications have shown promise to maintain the zeal to continue the therapy. Eye Tracking has been one of the popular interfaces to the HCI based rehabilitation gaming engines. Besides eye tracking also serves as a measure to evaluate the degree of neurological disorders and recovery from the same. vision based eye-tracking exists but are cost ineffective, besides being a privacy-disabled solution. Alternately, (Electrooculogram) EOG signals to track eye movements for HCI purpose has emerged and have shown potential. This work establishes real-time EOG signal devised eye and blink tracker system. Two channels of EOG signals are collected and supplied to motion tracker model. Random forest and LGBM classifier models with an accuracy of 96% were found suitable for real-time tracking. For pilot demonstration, the proposed EOG signal tracker system was interfaced with a rotating camera system that follows the eye movement to provide 360 degrees of vision at 2 degrees resolution. The real-time use-case of EOG eye tracker system paves way for integration with much more finer physiotherapy applications for paraplegia patients. The design files and EOG datasets for eye-tracking are made freely available for further usage to the researchers and users.

关键词： Paralysis EOG signals Machine Learning Random Forest Real time classification Eye Tracking Blinking Eye movements Paraplegia Physiotherapy Rehabilitation

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Autonomous robot Enhanced with Gemini Assistant for Elderly Care

Autonomous Robot Enhanced with Gemini Assistant for Elderly ...

引用

2024 international conference on Smart Electronics and Communication Systems, ISENSE 2024

作者： Nath, Akash Chacko, Chince Binu Hashim, Hanan P. Raghu, Harishanker Kiran, Surya John, Nimmy T. TIST Kerala India

ISBN: (纸本)9798331528126

Our intention in this paper is to develop a voice-assisted autonomous robot. This robot will be beneficial for elderly individuals and sick persons living alone. It will assist the person and automatically call predefined contacts in emergencies. In situations where an elderly person is living alone, the robot will assist throughout the day. It will ask questions periodically, and the woman can respond. The robot will send a warning signal to the designated contacts if there is no response. If the person falls or is injured in front of the robot, it will automatically call emergency services like the police and ambulance. The robot will also remind the person to take medications at the correct times and wake them up in the morning. If there is no response, it will send a warning signal to the designated contacts. The technological areas required for this project include natural language processing (NLP), speech recognition, text-to-speech (TTS), machine learning, and artificial intelligence. © 2024 IEEE.

关键词： robots

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Modular Method for Embodied Instruction Following with Environmental Context Adaptation

Modular Method for Embodied Instruction Following with Envir...

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international Joint conference on Neural Networks (IJCNN)

作者： Xu, Zhuoqun Ouyang, Liubo Liu, Yang Zhang, Li Hunan Univ Coll Comp Sci & Elect Engn Changsha Hunan Peoples R China Samsung Res China Beijing Beijing Peoples R China

ISBN: (纸本)9798350359329;9798350359312

Embodied instruction following (EIF) is a challenging task in Embodied AI that requires robots to possess a range of capabilities, including language understanding, object identification, environmental exploration, action planning, and accurate manipulation. To investigate household robot tasks that are considered meaningful in the near future, the community has already developed a primary solution based on a modular method, but its overall performance is far from human. Completing tasks in unfamiliar and unseen environments presents a significant challenge for intelligent robots. Error analysis of the ALFRED dataset indicates that the modular structure has shortcomings in target comprehension and vision-based interaction. As a result, we propose a post-processing optimization approach, which includes environment information alignment using semantic match and visual interaction enhancement with pose adjustment. Our paradigm is evaluated on two interactive datasets, and the performance is improved by an average of 37.12% (relative) compared to the baseline, suggesting that context information in the current environment effectively increases the adaptability of robots.

关键词： Embodied AI Human-robot Interaction Multimodal information processing Embodied Instruction Following

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Representation Extraction Using Hyperbolic Knowledge Distilled Framework - An Industrial Application on High Risk Environment 16

Representation Extraction Using Hyperbolic Knowledge Distill...

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16th international conference on Computer and Automation Engineering (ICCAE)

作者： Kumar, Vijeth Murugesan, Malathi Veneri, Giacomo Baker Hughes Bangalore India Baker Hughes Florence Italy

ISBN: (纸本)9798350370058;9798350370164

We propose a computer vision architecture based on Hyperbolic networks, contrastive learning and knowledge distillation to detect unsafe behavior in energy production and oil & gas plants. Data scarcity poses a significant challenge to develop machine learning applications in industry. Indeed, the data may be incomplete, inconsistent, or biased, making it difficult to develop accurate and reliable models. Insufficient data during training phase has direct impact on the models' representation learning capabilities;with the aid of vision Transformers (ViTs), we are able to solve data crunch situations by learning efficient representations of the existing data. We harnessed the power of ViTs, as it incorporates more global information, leading to quantitatively stronger intermediate feature representations. Further, we approached the task with contrastive learning and obtained pairs of samples which are similar, to tackle the limited data availability in our industrial use case. The proposed approach by applying hyperbolic embeddings helps in extracting complex relationships in the data. Furthermore, the size of the model makes it suitable for devices with low computational capabilities such as unmanned robots.

关键词： Hyperbolic networks Contrastive Learning and Knowledge Distillation Image processing Image Understanding vision Transformers (ViTs) Data Scarcity

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