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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR"

共 1569 条记录，以下是141-150 订阅

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Cross-Modal Person Re-identification for Service Robot 26

Cross-Modal Person Re-identification for Service Robot

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26th International conference on computer and Information Technology, ICCIT 2023

作者： Uddin, Md. Kamal Kuri, Ratnadip Shamim, Faria Rafi Ul Islam, Md. Zasim Uddin, Md. Hasan, Mahmudul Noakhali Science and Technology University Department of Computer Science and Telecommunication Engineering Noakhali Bangladesh Begum Rokeya University Department of Computer Science and Engineering Rangpur Bangladesh Comilla University Department of Computer Science and Engineering Comilla Bangladesh

ISBN: (纸本)9798350359015

In computer vision, cross-modal person re-identification (Re-ID) is an important task to recognize a person across the different sensors of a camera network in low light or dark environments. Elderly monitoring for nursing home in dark environments, especially at night, is a difficult task for human. However, for 24-hours monitoring, it is possible to monitor in low light or dark conditions via surveillance camera through service robot. To overcome this problem, we take advantage of depth sensor-based RGB-D cameras (e.g., Microsoft Kinect and Intel RealSense Depth Camera), which can be incorporated into robot. these cameras can capture RGB images in normal lighting conditions and depth images in low lighting conditions. In this paper, we propose a novel idea to monitor elderly people at night by utilizing cross-modal person re-identification for service robot. To reduce the modality gap between RGB and depth images, we extract local features from both modalities using Histogram of Oriented Gradients (HOG) as it can extract local shape information within an image. We validate our proposed method by performing experiments on two benchmark cross-modal re-identification datasets and gain significant recognition accuracy in cross domain. © 2023 ieee.

关键词： Cross-modal Person Re-identification Service Robot

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26th International conference on pattern recognition, ICPR 2022

26th International Conference on Pattern Recognition, ICPR 2...

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26th International conference on pattern recognition, ICPR 2022

ISBN: (纸本)9783031377419

the proceedings contain 49 papers. the special focus in this conference is on pattern recognition. the topics include: Towards Content-Aware Pixel-Wise Comic Panel Segmentation;data-Driven Transform Based Adaptive Compressive Sensing Framework for 3D Reconstruction of Forests;Land Cover Classification for Fires Using Sentinel-2 Satellite RGB Images and Deep Transfer Learning;landsat 8 data for forest fire monitoring: case of Mediouna forest in Tangier, Morocco;flying Wing Wildfire Tracking Using Visual Servoing and Gimbal Control;An Overview of Tools and Algorithms Used to Classify, Detect, and Monitor Forest Area Using LiDAR Data;Towards the Automation of Wildfire Monitoring with Aerial Vehicles: the FIREFRONT Project;real-Time Georeferencing of Fire Front Aerial Images Using Structure from Motion and Iterative Closest Point;forest Fires Identification Using Self-Supervised Learning;georeferencing High-Altitude Aerial Imagery;Wildfires Detection and Segmentation Using Deep CNNs and vision Transformers;a Method to Annotate Who Speaks a Text Line in Manga and Speaker-Line Dataset for Manga109;towards Multi-class Forest Floor Analysis;combining Automatic Speaker Verification and Prosody Analysis for Synthetic Speech Detection;towards Unconstrained Audio Splicing Detection and Localization with Neural Networks;comprint: Image Forgery Detection and Localization Using Compression Fingerprints;H4VDM: H.264 Video Device Matching;GBDF: Gender Balanced DeepFake Dataset Towards Fair DeepFake Detection;Misalignment Estimation in Non-Aligned Double JPEG Scenario Based on AC Histogram Analysis;attacking and Defending Printer Source Attribution Classifiers in the Physical Domain;descriptive Algorithmic Schemes as Image Analysis Machine Programs;application of Artificial Intelligence in Ophthalmology for Coagulate Map Formation to Carry Out Laser Eye Treatment;considering Meanings and Effects of Frames Without Onomatopoeias in Japanese Comics.

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From Points to Multi-Object 3D Reconstruction

From Points to Multi-Object 3D Reconstruction

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Engelmann, Francis Rematas, Konstantinos Leibe, Bastian Ferrari, Vittorio Rhein Westfal TH Aachen Aachen Germany Google Res Mountain View CA USA

ISBN: (纸本)9781665445092

We propose a method to detect and reconstruct multiple 31) objects from a single RGB image. the key idea is to optimize for detection, alignment and shape jointly over all objects in the RGB image, while focusing on realistic and physically plausible reconstructions. 'lb this end, we propose a key-point detector that localizes objects as center points and directly predicts all object properties, including 9-DoF bounding boxes and 3D shapes - all in a single forward pass. the proposed method formulates 3D shape reconstruction as a shape selection problem, i.e. it selects among exemplar shapes from a given database. this makes it agnostic to shape representations, which enables a lightweight reconstruction of realistic and visually-pleasing shapes based on CAD-models, while the training objective is formulated around point clouds and voxel representations. A collision-loss promotes non-intersecting objects, further increasing the reconstruction realism. Given the RGB image, the presented approach performs lightweight reconstruction in a single-stage, it is real-time capable, fully differentiable and end-to-end trainable. Our experiments compare multiple approaches for 9-DoF bounding box estimation, evaluate the novel shape-selection mechanism and compare to recent methods in terms of 3D bounding box estimation and 3D shape reconstruction quality.

关键词： Training Solid modeling three-dimensional displays Shape Databases Computational modeling Estimation

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Electronic System for Signage Detection 26

Electronic System for Signage Detection

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26th International conference on Electronics (ELECTRONICS)

作者： Molina, Cristian Saeteros, Morelva Iza, Danny Loja, Abraham Salesian Polytech Univ Quito Ecuador Univ Deusto Bizkaia Spain Natl Polytech Sch Quito Ecuador

ISBN: (纸本)9781665483216

the present work focuses on the need to give independence, security and generate a better lifestyle for people with visual disabilities, through their journeys abroad. the work seeks to be an aid so that people with visual disabilities can move better, recognizing useful signage according to standardized colors for prevention, obligation and information, for their safety and performance, such as pedestrian traffic lights, bus stops, pedestrian crossings, in addition, to evaluate the image detection methods using Machine Learning with deep learning techniques through the haar-cascade model to give a better recognition response to the user. the input to the system is a continuous video sequence, which analyzes and provides the user with an audible output by recognizing the different traffic signs at 2 meters. this process is based on an embedded system, which consists of a Raspberry Pi single board computer, Raspberry camera and headphones, the system was designed to be a low cost tool with a rechargeable battery that can be adapted to the white cane for the support and autonomy of people with visual disabilities.

关键词： Artificial vision OpenCV Match template Haar-classifier

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ABAW: Valence-Arousal Estimation, Expression recognition, Action Unit Detection & Emotional Reaction Intensity Estimation Challenges

ABAW: Valence-Arousal Estimation, Expression Recognition, Ac...

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ieee computer Society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Dimitrios Kollias Panagiotis Tzirakis Alice Baird Alan Cowen Stefanos Zafeiriou Queen Mary University of London UK Hume AI USA Imperial College London UK

the 5th ABAW Competition is part of the respective Workshop held in conjunction with ieee cvpr 2023 and is a continuation of the Competitions held at ECCV 2022, ieee cvpr 2022, ICCV 2021, ieee FG 2020 and cvpr 2017 conferences. It is dedicated at automatically analyzing affect. For this year’s Competition, we feature two corpora: i) an extended version of the Aff-Wild2 database and ii) the Hume-Reaction dataset. the former database is an audiovisual (A/V) one of around 600 videos of around 3M frames and is annotated for: a) two continuous affect dimensions, valence (how positive/negative a person is) and arousal (how active/passive a person is); b) basic expressions (e.g. happiness, neutral); and c) action units (i.e., facial muscle actions). the latter dataset is A/V in which reactions of individuals to emotional stimuli have been annotated for seven emotional expression intensities. thus the 5th ABAW Competition encompasses four Challenges: i) Valence-Arousal Estimation, ii) Expression Classification, iii) Action Unit Detection, and iv) Emotional Reaction Intensity Estimation. In this paper, we present these Challenges and their corpora, we outline the evaluation metrics and present the baseline systems and top performing teams’ per Challenge along with their obtained performance. More information for the Competition can be found in: https://***/resources/ cvpr-2023-5th-abaw.

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Alpha-Refine: Boosting Tracking Performance by Precise Bounding Box Estimation

Alpha-Refine: Boosting Tracking Performance by Precise Bound...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Yan, Bin Zhang, Xinyu Wang, Dong Lu, Huchuan Yang, Xiaoyun Dalian Univ Technol Sch Informat & Commun Engn Dalian Peoples R China Peng Cheng Lab Shenzhen Peoples R China Remark AI London England

ISBN: (纸本)9781665445092

Visual object tracking aims to precisely estimate the bounding box for the given target, which is a challenging problem due to factors such as deformation and occlusion. Many recent trackers adopt the multiple-stage strategy to improve bounding box estimation. these methods first coarsely locate the target and then refine the initial prediction in the following stages. However, existing approaches still suffer from limited precision, and the coupling of different stages severely restricts the method's transferability. this work proposes a novel, flexible, and accurate refinement module called Alpha-Refine (AR), which can significantly improve the base trackers' box estimation quality. By exploring a series of design options, we conclude that the key to successful refinement is extracting and maintaining detailed spatial information as much as possible. Following this principle, Alpha-Refine adopts a pixel-wise correlation, a corner prediction head, and an auxiliary mask head as the core components. Comprehensive experiments on TrackingNet, LaSOT, GOT-10K, and VOT2020 benchmarks with multiple base trackers show that our approach significantly improves the base tracker's performance with little extra latency. the proposed Alpha-Refine method leads to a series of strengthened trackers, among which the ARSiamRPN (AR strengthened SiamRPNpp) and the ARDiMP50 (AR strengthened DiMP50) achieve good efficiency-precision trade-off while the ARDiMPsuper (AR strengthened DiMPsuper) achieves very competitive performance at a realtime speed. Code and pretrained models are available at hups://***/MasterBin-IIALI/Alphalteline.

关键词： Couplings Visualization computer vision Target tracking Correlation Computational modeling Estimation

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Diffuse, Attend, and Segment: Unsupervised Zero-Shot Segmentation using Stable Diffusion

Diffuse, Attend, and Segment: Unsupervised Zero-Shot Segment...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Junjiao Tian Lavisha Aggarwal Andrea Colaco Zsolt Kira Mar Gonzalez-Franco Georgia Institute of Technology Google

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Producing quality segmentation masks for images is a fundamental problem in computer vision. Recent research has explored large-scale supervised training to enable zero- shot transfer segmentation on virtually any image style and unsupervised training to enable segmentation without dense annotations. However, constructing a model capable of segmenting anything in a zero-shot manner without any anno-tations is still challenging. In this paper, we propose to uti-lize the self-attention layers in stable diffusion models to achieve this goal because the pre-trained stable diffusion model has learned inherent concepts of objects within its attention layers. Specifically, we introduce a simple yet ef-fective iterative merging process based on measuring KL divergence among attention maps to merge them into valid segmentation masks. the proposed method does not re-quire any training or language dependency to extract qual-ity segmentation for any images. On COCO-Stuff-27, our method surpasses the prior unsupervised zero-shot trans-fer SOTA method by an absolute 26% in pixel accuracy and 17% in mean IoU. the project page is at https://***/view/diffseg/home. 1 1 Georgia Institute of Technology

关键词： Training Image segmentation computer vision Satellites Merging Diffusion models pattern recognition

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the 6th Affective Behavior Analysis in-the-wild (ABAW) Competition

The 6th Affective Behavior Analysis in-the-wild (ABAW) Compe...

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ieee computer Society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Dimitrios Kollias Panagiotis Tzirakis Alan Cowen Stefanos Zafeiriou Irene Kotsia Alice Baird Chris Gagne Chunchang Shao Guanyu Hu Queen Mary University of London UK Hume AI USA Imperial College London UK Cogitat UK Xi’an Jiaotong University China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

this paper describes the 6th Affective Behavior Analysis in-the-wild (ABAW) Competition, which is part of the respective Workshop held in conjunction with ieee cvpr 2024. the 6th ABAW Competition addresses contemporary challenges in understanding human emotions and behaviors, crucial for the development of human-centered technologies. In more detail, the Competition focuses on affect related benchmarking tasks and comprises of five sub-challenges: i) Valence-Arousal Estimation (the target is to estimate two continuous affect dimensions, valence and arousal), ii) Expression recognition (the target is to recognise between the mutually exclusive classes of the 7 basic expressions and ’other’), iii) Action Unit Detection (the target is to detect 12 action units), iv) Compound Expression recognition (the target is to recognise between the 7 mutually exclusive compound expression classes), and v) Emotional Mimicry Intensity Estimation (the target is to estimate six continuous emotion dimensions). In the paper, we present these Challenges, describe their respective datasets and challenge protocols (we outline the evaluation metrics) and present the baseline systems and top performing teams’ per Challenge, as well as their obtained performance. More information for the Competition can be found in: https://***/6th.

关键词： Measurement Emotion recognition computer vision Protocols Target recognition Databases conferences

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A Deep Learning Approach for Satellite and Debris Detection: YOLO in Action 26

A Deep Learning Approach for Satellite and Debris Detection:...

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26th International conference on computer and Information Technology, ICCIT 2023

作者： Ahamed, Md Younus Bin Syed, Md Asif Chatterjee, Paroma Bin Habib, Al Zadid Sultan West Virginia University Lane Department of Computer Science and Electrical Engineering MorgantownWV26506 United States West Virginia University Department of Industrial and Management Systems Engineering MorgantownWV26506 United States

ISBN: (纸本)9798350359015

Satellite detection refers to the identification and recognition of satellites in satellite images. this task is crucial for various applications, including space surveillance, satellite tracking, and space debris monitoring. Several studies have been conducted to develop methods and techniques for satellite detection. the intricacy and scale of satellite images make satellite detection incredibly challenging. the process of manual identification and classification necessitates a significant amount of time. Recently, Deep Learning (DL) has emerged as a highly promising approach in computer vision (CV) tasks. this study evaluates the effectiveness of three widely employed DL-based object detection algorithms, specifically YOLOv4, YOLOv5, and YOLOv8, within the field of satellite detection. the algorithms have undergone training utilizing a dataset of satellite photos comprising both satellites and debris. the evaluation of the algorithm's performance involved the utilization of broadly accepted metrics, including Precision, Recall, Mean Average Precision (mAP), Box Loss, and Class Loss. the findings indicated that all three algorithms exhibited a high level of accuracy in detecting and precisely determining the location of objects inside satellite data. the performance of YOLOv5 and YOLOv8 surpassed that of YOLOv4 across various measures, with a notable advantage observed in detecting tiny objects. Overall, this study demonstrates the capabilities of DL algorithms in the context of satellite detection and classification techniques. © 2023 ieee.

关键词： Debris Object Detection Satellite YOLOv4 YOLOv5 YOLOv8

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STMTrack: Template-free Visual Tracking with Space-time Memory Networks

STMTrack: Template-free Visual Tracking with Space-time Memo...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Fu, Zhihong Liu, Qingjie Fu, Zehua Wang, Yunhong Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing Peoples R China Beihang Univ Hangzhou Innovat Inst Beijing Peoples R China

ISBN: (纸本)9781665445092

Boosting performance of the offline trained siamese trackers is getting harder nowadays since the fixed information of the template cropped from the first frame has been almost thoroughly mined, but they are poorly capable of resisting target appearance changes. Existing trackers with template updating mechanisms rely on time-consuming numerical optimization and complex hand-designed strategies to achieve competitive performance, hindering them from real-time tracking and practical applications. In this paper, we propose a novel tracking framework built on top of a space-time memory network that is competent to make full use of historical information related to the target for better adapting to appearance variations during tracking. Specifically, a novel memory mechanism is introduced, which stores the historical information of the target to guide the tracker to focus on the most informative regions in the current frame. Furthermore, the pixel-level similarity computation of the memory network enables our tracker to generate much more accurate bounding boxes of the target. Extensive experiments and comparisons with many competitive trackers on challenging large-scale benchmarks, OTB-2015, TrackingNet, GOT-10k, LaSOT, UAV123, and VOT2018, show that, without bells and whistles, our tracker outperforms all previous state-of-the-art real-time methods while running at 37 FPS.

关键词： Visualization computer vision Target tracking Codes Adaptive systems Benchmark testing Boosting

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