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EdgeSharing: Edge Assisted Real-time Localization and Object Sharing in Urban Streets 40

EdgeSharing: Edge Assisted Real-time Localization and Object...

引用

40th ieee conference on Computer communications (ieee INFOCOM)

作者： Liu, Luyang Gruteser, Marco Google Res Mountain View CA 94043 USA Rutgers State Univ Winlab New Brunswick NJ USA

ISBN: (纸本)9780738112817

Collaborative object localization and sharing at smart intersections promises to improve situational awareness of traffic participants in key areas where hazards exist due to visual obstructions. By sharing a moving object's location between different camera-equipped devices, it effectively extends the vision of traffic participants beyond their field of view. However, accurately sharing objects between moving clients is extremely challenging due to the high accuracy requirements for localizing both the client position and positions of its detected objects. Therefore, we introduce EdgeSharing, a localization and object sharing system leveraging the resources of edge cloud platforms. EdgeSharing holds a real-time 3D feature map of its coverage region to provide accurate localization and object sharing service to the client devices passing through this region. We further propose several optimization techniques to increase the localization accuracy, reduce the bandwidth consumption and decrease the offloading latency of the system. The result shows that the system is able to achieve a mean vehicle localization error of 0.28-1.27 meters, an object sharing accuracy of 82.3%-91.4%, and a 54.7% object awareness increment in urban streets and intersections. In addition, the proposed optimization techniques reduce bandwidth consumption by 70.12% and endto-end latency by 40.09%.

关键词： Location awareness Meters visualization Three-dimensional displays image edge detection Collaboration Bandwidth

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Joint Semantic Information Gathering and Broadcasting for Multi-task Edge processing

Joint Semantic Information Gathering and Broadcasting for Mu...

引用

ieee International conference on communications in China (ICCC)

作者： Huawei Hou Suzhi Bi Xian Li Xiaohui Lin College of Electronics and Information Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

In this paper, we propose an edge computing system where an edge server (ES) gathers multi-media sensing data from collaborative sensing devices (SDs) in upstream and broadcasts the sensing information to downstream user devices that perform different tasks, e.g., image segmentation and saliency detection. In particular, we leverage the wisdom of semantic communication that applies joint source-channel coding (JSCC) techniques to extract and transmit only the task-relevant information contained in the sensing data to reduce the communication and computation workload. To efficiently train the upstream and downstream encoders/decoders involved in the system, instead of performing end-to-end training, we propose a task-oriented training strategy that decouples the training into two separate steps without compromising the performance. We conduct experiments using open-source multi-task datasets under varying channel signal-to-noise power ratios (SNRs). The simulation results show that, compared to the representative benchmark approaches, the proposed method can effectively reduce $\mathbf{9 2. 6 \%} / 75 \%$ uplink/downlink data workload and elegantly balance the performance of different tasks.

关键词： Training Simulation Semantics Multitasking Feature extraction Sensors Data mining

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Polynomial image Enlargement based on Edge Detection 5

Polynomial Image Enlargement based on Edge Detection

引用

5th ieee Advanced Information Technology, Electronic and Automation Control conference, IAEAC 2021

作者： Zhou, Hao Shandong Technology and Business University School of Computer Science and Technology Yantai China

ISBN: (纸本)9781728180281

This paper proposes an image enlargement method that approximates the original image scene by constructing a polynomial surface and using edges information as a feature constraint to solve the problem that can not virtually guarantee edge sharpening during the image enlargement process. First, we use the detection operator to preprocess the image and save the high-frequency information of the edges. Second, we use the sampling formula and edge information of the image as constraints to construct the fitting surface patch. The new method uses edge high-frequency information as suggestions for interpolation polynomials. Experiments and the comparison between other methods show that the images generated by this method have extraordinary visual effects, which are beneficial to medical image processing and other engineering applications and practices. © 2021 ieee.

关键词： Polynomials

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Towards Real Time Traffic Analysis with Reduced image Quality

Towards Real Time Traffic Analysis with Reduced Image Qualit...

引用

ieee India conference (INDICON)

作者： Rohit Mohanraj Erunika Oshani Dayaratna Department of Electronics and Communication Engineering National Institute of Technology Warangal Warangal India Department of Statistics and Computer Science Faculty or science University of peradeniya SriLanka

ISBN: (数字)9798350391282

ISBN: (纸本)9798350391299

This paper presents a detailed study of two computationally efficient object recognition models-YOLOv5 and Fast R-CNN with a MobileNet backbone-focusing on their performance under different degrees of image / video qualities. Blurring is a common problem in autonomous driving due to motion, weather conditions, and other factors, which can greatly reduce picture quality and, consequently, the accuracy of object recognition. Also, the low-quality images produced by on-car devices suffer when trying to utilize them for advanced detection applications. Utilizing the Zenseact Open Dataset (ZOD), images are generated by varying kernel sizes of Gaussian blur to examine how each model's recognition accuracy is affected by picture degradation. Our study shows key differences in the stability of these models, showing the trade-offs between processing speed and recognition confidence in poor visual settings. The results provide helpful guidance for choosing object recognition systems that keep performance in difficult situations, crucial for the safety and efficiency of driverless cars.

关键词： YOLO Performance evaluation Degradation Training Adaptation models visualization Accuracy Real-time systems Object recognition Video recording

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A Bio-Inspired Computational Model for the3D Motion Estimation

A Bio-Inspired Computational Model for the3D Motion Estimati...

引用

Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), ieee conference on

作者： Di Zhang Lu Ping Wu Qi Long Ye Communication University of China Beijing University of Posts and Telecommunications

ISBN: (数字)9798350374490

ISBN: (纸本)9798350374506

Stereoscopic motion perception is pivotal for human-world interaction and has sparked advancements in artificial vision technologies. We introduce the Stereo Motion Perception Model (SMPM), a bio-inspired computational model designed to emulate the process of stereoscopic motion perception inherent in the human visual system. The SMPM extracts temporal, spatial, and shape features from stereoscopic video stimuli, enabling accurate motion perception. The performance of SMPM is tested and compared with human perception. The results indicates that the SMPM effectively estimates target motion on both simplistic and complex scene, high consistency is shown with human stereo motion perception. Future implications of the bio-inspired model is discussed.

关键词： Solid modeling visualization Three-dimensional displays Computational modeling Stereo image processing Biological system modeling Virtual reality

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Improving Myanmar image Caption Generation Using NASNetLarge and Bi-directional LSTM

Improving Myanmar Image Caption Generation Using NASNetLarge...

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Computer Applications (ICCA), ieee conference on

作者： San Pa Pa Aung Win Pa Pa Tin Lay Nwe Natural Language Processing Lab University of Computer Studies Yangon Yangon Myanmar Visual Intelligence Department Institute for Infocomm Research Singapore

The main objective of this paper is to improve the automatic Myanmar captions by learning the contents of images using NASNetLarge and Bi-LSTM model. Describing the contents of an image is a complex task for machine without human intervention. Computer Vision and Natural Language processing are widely used to tackle this problem. This paper proposed a deep learning-based Myanmar image captioning system which used a NASNetLarge feature extraction model of CNN as an encoder and a deep Recurrent Neural Network (RNN) with Bi-directional Long Short-Term Memory (LSTM) as a decoder. For corpus construction, we created and annotated the Myanmar image captions corpus (consists of over 40k Myanmar sentences), which is based on Flickr8k dataset. Furthermore, two different types of segmentations such as word segmentation level and syllable segmentation level are studied in text preprocessing step. In this work, the proposed Bi-directional LSTM model is compared with LSTM, GRU as well as the baseline model. Experiments on the updated dataset is presented that all of our models using syllable segmentation give higher and comparable BLEU scores than word segmentation for Myanmar image captioning system. NASNetLarge with Bi-directional LSTM model using syllable segmentation approach achieved the highest BLEU-4 score 40.05% which is 12.5% better than word segmentation in this work and 15.67% BLEU-4 score better than our previous work.

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PeT-KeyStAtion: Parameter-efficient Transformer with Keypoint-guided Spatial-temporal Aggregation for Video-based Person Re-identification

PeT-KeyStAtion: Parameter-efficient Transformer with Keypoin...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Xingan Ma Jinhui Yi Juergen Gall University of Bonn Bonn Germany

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Video-based Person Re-identification (ReID) is crucial in visual surveillance, focusing on matching video snippets of individuals across multiple non-overlapping cameras. Existing methods either conduct ReID at the image level without leveraging temporal information, or employ complex temporal information aggregation techniques, which results in substantial network size and reduced performance efficiency. Recent advances in Vision Transformer (ViT) architectures leverage diverse large-scale datasets alongside sophisticated architectures to achieve enhanced fine-grained feature discrimination. To fully explore the potential of ViT architectures without adding substantial additional modules for video-based ReID, we propose PeT-KeyStAtion: a Parameter-efficient Transformer with Keypoint- guided Spatial-temporal Aggregation using a Spatial-Temporal and Keypoint (STK) Module with lightweight adapters. Our framework effectively captures and aggregates spatial, temporal, and keypoint information with only 11% of the parameters compared to full fine-tuning. Extensive experiments show that our method outperforms state-of-the-art baselines on MARS and iLIDS-VID, and achieves promising performance on LS-VID.

关键词： Representation learning visualization Mars Computer vision Surveillance Focusing Signal processing Transformers Speech processing Identification of persons

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SimpleISP-YOLO: A Lightweight Model for Low-Light image Enhancement and Object Detection by Simulating Simplified ISP Process 1

引用

8th International conference on Life System Modeling and Simulation, LSMS 2024 and 8th International conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Liao, Haoren Chen, Ling Yang, Bo College of Engineering and Design Hunan Normal University Changsha410000 China

ISBN: (数字)9789819603138

ISBN: (纸本)9789819603121

In recent years, the field of object detection has seen the rise of numerous remarkable models due to the advancement of deep learning technologies. Low-light conditions have historically posed a significant challenge in the domain of object detection. The quality of the initial imagery plays a pivotal role in determining the efficacy of subsequent object detection algorithms. Notably, in dimly lit settings, the captured images are prone to heightened noise, color aberrations, and various other challenges. To address these issues, the current study presents a novel low-light image enhancement framework, designated as SimpleISP-Net. Inspired by image Signal processing (ISP), SimpleISP-Net simulates the simplified ISP process by performing only color correction and gamma correction on low-light images to achieve image enhancement. By integrating SimpleISP-Net with the YOLOv5s detection model, an end-to-end low-light object detection model called SimpleISP-YOLO is then constructed. The experimental results show that the algorithm proposed in this paper achieved an mAP of 68.1% on the low-light target detection dataset ExDark, which is an improvement of 11.4% compared to the original YOLOv5s model, with only a slight increase in computational complexity by 2.83 GFLOPs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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Detection of Diabetic Retinopathy at Early Stage Using Retinal Fundus images

Detection of Diabetic Retinopathy at Early Stage Using Retin...

引用

International conference on Electronics, communications and Information Technology (ICECIT)

作者： Zaman, Tanha Hossain, Quazi Delwar Chittagong Univ Engn & Technol Dept Elect & Elect Engn Chittagong Bangladesh

ISBN: (纸本)9781665423632

Diabetic retinopathy is among the most widespread disorder that affects the eyes and primary basis of ocular breakage in industrialized nations. The presence of microaneurysms (1VIAs) on the retina is amongst the most significant aspects of this medical condition. Microaneurysms in the eyelet are among first manifestation of diabetic retinopathy, therefore is among the top reasons of visual impairment. Here, we present a new microaneurysm (MA) detection technique employing on color fundus images for early diabetic retinopathy assessment. The pipeline of preprocessing, feature extraction, classification, and postprocessing achieves significant gains over the traditional MA detection technique. This study further provides an integrated approach for detecting and predicting the prevalence of diabetic retinopathy premised on close assessment of the actual quantity of microaneurysms.

关键词： Diabetic Retinopathy Fundus image Microaneurysms Feature extraction

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image Dehazing using Dark Channel Prior 3

Image Dehazing using Dark Channel Prior

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3rd International conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Chowdari, Ch. Pratyusha Kashyap, V Dheeraj Kumar, K Kiran Charan, M Raghu Akhil, B. Griet Dept.ECE Hyderabad India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Windstorms, foggy winters, and sand storms generally degrade image quality and have an impact on computer vision applications, which could be a safety concern for drivers because of light dispersing and retention by dust remnant or air molecules, making the view hazy or foggy. Our method relies on the Dark Channel Prior (DCP) methodology, which improves visual contrast too. Upon quantifying the ambient light, the value is utilized for calculating transmission estimate. The transmission map is fine-tuned afterwards, and even the image is recovered. By processing locally, we simplify the refining stage and localize the refinement zone by working with the coarse dark channel. This strategy will reduce blurriness while restoring the true brilliance of distant objects. To compare we have chosen different existing methods like All-in-One Dehazing Network (AOD-net), Convolutional Neural Network (CNN), High Resolution de-hazer(HR de-hazer) and make the comparison using metrics like Peak Signal to Noise Ratio(PSNR) and Structural Similarity Index (SSIM). Not only in terms of quality parameters but we have also checked over the latency period where we found DCP is giving a valuable output. Hence, the Peak Signal to Noise Ratio value of our proposed method is 20% greater than that of existing methods. © 2022 ieee.

关键词： Signal to noise ratio

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