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Advanced Human Pose Estimation and Event Classification Using Context-Aware Features and XGBoost Classifier

IEEE ACCESS 2024年 12卷 179839-179856页

作者： Wahid, Wasim Alarfaj, Aisha Ahmed Alabdulqader, Ebtisam Abdullah Sadiq, Touseef Rahman, Hameedur Jalal, Ahmad Air Univ Fac Comp & AI Islamabad 44000 Pakistan Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia Qassim Univ Coll Art & Humanities Sci Comp Sci Dept Buraydah 52571 Saudi Arabia Univ Agder Ctr Artificial Intelligence Res Dept Informat & Commun Technol N-4879 Grimstad Norway Korea Univ Coll Informat Dept Comp Sci & Engn Seoul 02841 South Korea

This paper presents an advanced approach to Human Pose Estimation (HPE) and Semantic Event Classification (SEC), emphasizing the need for sophisticated human skeleton models, context-aware feature extraction, and machine learning techniques for precise event recognition in daily life logs. HPE, crucial in applications like sports analysis and surveillance systems, involves predicting human joint locations from images and videos. Recent deep learning advancements have significantly improved HPE, particularly in crowded scenes and occlusion challenges. Despite many surveys, a comprehensive review of HPE, especially with recent deep learning innovations, is still needed. Our research addresses this by proposing a novel HPE and SEC system. The system begins with preprocessing steps, including converting videos into image sequences, applying sliding window techniques, and converting images to grayscale, then extracting human silhouettes using binary masks. We use the GrabCut algorithm for human detection and perform skeletonization with Hough transform algorithm. Keypoint detection is achieved through pose estimation, and full-body feature extraction includes using OpenPose for movable body parts, the Lucas-Kanade method for a 3D Cartesian view, and Texton Map techniques. Key point features are further characterized using motion histograms, pose landmark visualization and Local Intensity Order pattern (LIOP) features. The system is optimized with adaptive moment estimations and classified using the XGBoost Classifier. Evaluation on the COCO, UCF50, and YouTube datasets showed classification accuracies of 92.90%, 90.9%, and 91.2%, respectively, demonstrating our approach's superior performance and effectiveness compared to existing state-of-the-art techniques.

关键词： Feature extraction Accuracy Pose estimation Three-dimensional displays Deep learning Training Lighting Videos Sports Semantics event recognition remote sensing multi-level feature extraction Lucas-Kanade skeletonization XGBoost machine learning

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Improved CNN-based CatBoost model for license plate remote sensing image classification

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SIGNAL processing 2023年第1期213卷

作者： Zhang, Songhua Lu, Xiuling Lu, Zhangjie Hunan Inst Technol Sch Elect & Informat Engn Hengyang 421002 Peoples R China

The rapid development of artificial intelligence technology has brought profound technical changes to many traditional industries, among which intelligent transportation has become a hot spot for development in the traditional transportation field. remote sensing license plate image recognition technology is extensively emphasized in such domains as intelligent transportation and intelligent vehicle management. However, in the practical traffic environment, low visibility scenes caused by complex environmental factors such as rain, snow, haze and cloudy days influence the recognition and classification of license plates, while the distortion of license plate images that may be caused by irregular movements of vehicles bring challenges to license plate recognition classification. The CNN-CatBoost model proposed in this paper divides the license plate recognition classification into two stages. The first stage uses the excellent performance of convolutional neural network in processing image data to extract various license plate image features;the second stage uses the CatBoost module to further process the image feature data and finally obtain the remote sensing license plate information. The model achieves outstanding results in the experiments and has practical application value. Through comparison with other network models, the CNN-CatBoost model proposed in this paper has superior performance.

关键词： remote sensing License plate image classification CatBoost Convolutional neural network Deep learning

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Adaptive Component Discrimination Network for Airplane Detection in remote sensing images

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND remote sensing 2021年 14卷 7699-7713页

作者： Jia, Hecheng Guo, Qian Chen, Jin Wang, Feng Wang, Haipeng Xu, Feng Fudan Univ Sch Informat Sci & Technol Minist Educ Key Lab Informat Sci Electromagnet Waves Shanghai 200433 Peoples R China Beijing Inst Remote Sensing Informat Beijing Peoples R China

Airplane detection and recognition in the high-resolution remote sensing images (RSIs) remain a challenging task due to the factors of multiple view angles, multiple scales, multiple orientations, etc. This article proposes an adaptive component discrimination network for airplane detection and recognition in RSIs, which focuses on various scales from global to local, making full use of the overall contour as well as the dominant component features of airplanes. First, a standardization processing module is proposed for image projection conversion and resolution uniform to alleviate the confusion of different types of airplanes in different resolution images. Second, the rotatable boundingbox-based pyramid network is utilized to extract candidate airplane coordinates and categories. Furthermore, an adaptive aircraft component discrimination method is established for confusing few-shot airplane targets recognition, which consists of a target orientation adaptive adjustment module (OAAM) and a component discrimination module (CDM). OAAM obtains airplanes with the same orientations by predicting the orientation of the slices and rotating them adaptively. All the uniformed slices are then fed into the CDM for dominant components detection, which corrects the target preclassification results, improving the classification performance. Experiments conducted on the 2020 Gaofen Challenge demonstrate the efficacy and superiority of the proposed method.

关键词： Airplanes Feature extraction Object detection Task analysis Target recognition Detectors image resolution Airplane detection and recognition component discrimination target orientation adaptive adjustment

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Object-oriented remote sensing image information extraction method based on multi-classifier combination and deep learning algorithm

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pattern recognition LETTERS 2021年 141卷 32-36页

作者： Tan, Qulin Guo, Bin Hu, Jun Dong, Xiaofeng Hu, Jiping Beijng Jiaotong Univ Sch Civil Engn Beijing 100044 Peoples R China Beijng Jiaotong Univ Inst Spatial Informat Line Engn Beijing 100044 Peoples R China Weifang Engn Vocat Coll Dept Architectural Engn Weifang 262500 Shandong Peoples R China Beijng Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China Beijng Jiaotong Univ Acad Urban Planning & Design Beijing 100044 Peoples R China

In recent years, high spatial resolution remote sensing technology has made significant progress. High resolution remote sensing satellites provide great convenience for high-quality image acquisition. In order to adapt to changes in the appearance of the target, mainstream tracking algorithms often use pattern recognition methods to build a target appearance model with learning capabilities, and use the image frames acquired during the tracking process to update the appearance model. This paper mainly studies the object-oriented remote sensing image information extraction method based on multi-classifier combination and deep learning algorithm. In this paper, we use the splitting mechanism of the tree structure to retain the appearance model with diversity, and through the integrated learning integration strategy, the target position is collaboratively predicted. Through the comparative analysis on the OTB and VOT platforms, the algorithm works well when the requirements of the tracking standards are low (the accuracy threshold is greater than 20 pixels and the success threshold is less than 0.4 pixels). The experimental results in this paper show that compared with other advanced classification methods, the proposed method shows better generalization performance in accuracy, recall, f-measure, g-mean and AUC. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Multi-classifier combination Deep learning algorithm Object-oriented remote sensing image Information extraction

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Unsupervised Cross-Domain Network Based on Bi-classifier for PolSAR image Classification 6

Unsupervised Cross-Domain Network Based on Bi-classifier for...

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6th International Conference on Artificial Intelligence and pattern recognition, AIPR 2023

作者： Hua, Wenqiang Liu, Lin Sun, Nan Wang, Chen Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

Polarimetric Synthetic Aperture Radar (PolSAR) image classification is an important research content in the field of remote sensing. However, the lack of effective labeled data and the serious class distribution shift problem between different datasets hinder the application of deep learning networks in PolSAR image classification. Therefore, this paper proposes a Bi-classifier adversarial network based on covariance difference matrix and Convolutional Block Attention Module (CBAM) to solve the cross-domain problem in PolSAR images. Firstly, since PolSAR image processing generally uses the covariance matrix or coherence matrix of a single target pixel to construct the data set, which cannot effectively use the local information of PolSAR data,Therefore, the PolSAR coherence difference matrix P is proposed as the description of the PolSAR image difference information. Secondly, due to the different importance of features between neighboring pixels, the CBAM mechanism is introduced in order to exploit the spatial information of PolSAR data. Finally, in order to solve the cross-domain problem of PolSAR images more effectively, a bi-classifier adversarial network based on covariance difference matrix and CBAM is proposed. In this paper, we conduct experiments on public datasets, using images of San FranciscoII acquired in 2008 as the source domain, and images of San Francisco II acquired in 1992 as the target domain data. The experimental results show that the proposed method can effectively solve the cross-domain problem of PolSAR images. © 2023 ACM.

关键词： image classification

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A KNOWLEDGE DISTILLATION METHOD BASED ON IQE ATTENTION MECHANISM FOR TARGET recognition IN SAR imageRY

A KNOWLEDGE DISTILLATION METHOD BASED ON IQE ATTENTION MECHA...

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IEEE International Geoscience and remote sensing Symposium (IGARSS)

作者： Wang, Jielei Jiang, Ting Cui, Zongyong Cao, Zongjie Cao, Changjie Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China

ISBN: (数字)9781665427920

ISBN: (纸本)9781665427920

The huge computing and storage requirements of deep convolutional neural networks (DCNNs) limit their application on edge computing devices. In this article, we propose an attention mechanism based on the feature map quality evaluation algorithm (IQE). The knowledge distillation method based on the IQE attention mechanism uses the IQE method to identify important knowledge in the pre-trained SAR target recognition deep neural network. Then in the process of knowledge distillation, the lightweight network is forced to focus on the learning of important knowledge. Through this mechanism, the method proposed in this paper can efficiently transfer the knowledge of the pre-trained SAR target recognition network to the lightweight network, which makes it is possible to deploy the SAR target recognition algorithm on the edge computing platform. Comparison experiments with several commonly used knowledge distillation methods have proved the effectiveness of our proposed method. In addition, we also verified the performance of the lightweight network obtained by our method on the edge platform based on the K210 processor.

关键词： Deep convolutional neural network SAR-ATR Knowledge distillation image quality evaluation algorithm Knowledge processing Unit (KPU)

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Learning consensus-aware semantic knowledge for remote sensing image captioning

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pattern recognition 2024年 145卷

作者： Li, Yunpeng Zhang, Xiangrong Cheng, Xina Tang, Xu Jiao, Licheng Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Shaanxi Peoples R China

Tremendous progresses have been made in remote sensing image captioning (RSIC) task in recent years, yet there still some unresolved problems: (1) facing the gap between the visual features and semantic concepts, (2) reasoning the higher-level relationships between semantic concepts. In this work, we focus on injecting high-level visual-semantic interaction into RSIC model. Firstly, the semantic concept extractor (SCE), end-to end trainable, precisely captures the semantic concepts contained in the RSIs. In particular, the visual-semantic co-attention (VSCA) is designed to grain coarse concept-related regions and region-related concepts for multi modal interaction. Furthermore, we incorporate the two types of attentive vectors with semantic-level relational features into a consensus exploitation (CE) block for learning cross-modal consensus-aware knowledge. The experiments on three benchmark data sets show the superiority of our approach compared with the reference methods.

关键词： Cross-modal understanding Visual-semantic interaction remote sensing image captioning Graph convolutional network

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Multi-scale extraction and spatial analysis of growth pattern changes in urban water bodies using sentinel-2 MSI imagery: a study in the central part of India

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2024年第6期38卷 2397-2423页

作者： Vohra, Rubeena Kumar, Ashish Rongali, Gopinadh Bharati Vidyapeeths Coll Engn Dept Elect & Commun Engn New Delhi India Bennett Univ Sch Comp Sci Engn & Technol Greater Noida Uttar Pradesh India Natl Ctr Medium Range Weather Forecasting NCMRWF New Delhi India

Changes in the environmental conditions of Land Use/Land Cover can also significantly influence surface water, mostly due to the dramatically increased biophysical characteristics of the land surfaces. Therefore, extracting multi-scale surface water bodies in urbanized areas is essential. Besides, the past relationships of surface water bodies mostly used low-resolution images. However, the water surface mapping and spatial analysis of multi-scale structure changes in urban areas with high-resolution satellite images have not yet been studied. The present study focused on multi-scale extraction and spatial analysis of growth pattern changes in urban water bodies using Sentinel-2 MSI imagery. Satellite data for the study were obtained from the location map of Chhattisgarh, in the central part of India. The available Sentinel-2 images covering the study area over the period 2012-2020. Initially, we applied image pre-processing steps to remove the shadow noise and distortions and convert the images into a suitable form for mapping. Then, the water surface mapping process combines multi-band water indices (Normalized Difference Vegetation Index and Normalized Difference Water Index (NDWI), Modified NDWI, Urban Difference WI, and Urban Difference Shadow Index and object-oriented methods to extract multi-scale water body information using high-resolution images. Study periods for change analysis were divided into sets. (a) Inter-annual variation is evaluated using the Water Area Frequency Index (WAFI) covering the entire study area over the time period 2012-2020. (b) The spatial variation of land use patterns (i.e., changes in the growth pattern of different scales) in the selected water areas is assessed using eight landscape metrics over three different time periods (2012, 2015, and 2020). These two variations in the WAFI layer are post-processed to determine three multi-scale water scenarios (rivers, streams, canals, and reservoirs): (1) Artificial Waterway (AW of cana

关键词： Land use and land cover (LULC) Sentinel-2 Urban water bodies Multi-scale extraction Landscape metrics Multi-band water indices (MWI) remote sensing

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DustNet: Attention to Dust 45th

DustNet: Attention to Dust

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45th Annual Conference of the German-Association-for-pattern-recognition (DAGM GCPR)

作者： Michel, Andreas Weinmann, Martin Schenkel, Fabian Gomez, Tomas Falvey, Mark Schmitz, Rainer Middelmann, Wolfgang Hinz, Stefan Fraunhofer Inst Optron Syst Technol & Image Explo Karlsruhe Germany Karlsruhe Inst Technol Inst Photogrammetry & Remote Sensing Karlsruhe Germany Meteodata Santiago Chile

ISBN: (纸本)9783031546044;9783031546051

Detecting airborne dust in common RGB images is hard. Nevertheless, monitoring airborne dust can greatly contribute to climate protection, environmentally friendly construction, research, and numerous other domains. In order to develop an efficient and robust airborne dust monitoring algorithm, various challenges have to be overcome. Airborne dust may be opaque as well translucent, can vary heavily in density, and its boundaries are fuzzy. Also, dust may be hard to distinguish from other atmospheric phenomena such as fog or clouds. To cover the demand for a performant and reliable approach for monitoring airborne dust, we propose DustNet, a dust density estimation neural network. DustNet exploits attention and convolutional-based feature pyramid structures to combine features from multiple resolution and semantic levels. Furthermore, DustNet utilizes highly aggregated global information features as an adaptive kernel to enrich high-resolution features. In addition to the fusion of local and global features, we also present multiple approaches for the fusion of temporal features from consecutive images. In order to validate our approach, we compare results achieved by our DustNet with those results achieved by methods originating from the crowd-counting and the monocular depth estimation domains on an airborne dust density dataset. Our DustNet outperforms the other approaches and achieves a 2.5% higher accuracy in localizing dust and a 14.4% lower mean absolute error than the second-best approach.

关键词： Dust Monitoring Visual Regression Deep Learning

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OmniCity: Omnipotent City Understanding with Multi-level and Multi-view images

OmniCity: Omnipotent City Understanding with Multi-level and...

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IEEE/CVF Conference on Computer Vision and pattern recognition (CVPR)

作者： Li, Weijia Lai, Yawen Xu, Linning Xiangli, Yuanbo Yu, Jinhua He, Conghui Xia, Gui-Song Lin, Dahua Sun Yat Sen Univ Guangzhou Peoples R China SenseTime Res Hong Kong Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China Wuhan Univ Wuhan Peoples R China

ISBN: (纸本)9798350301298

This paper presents OmniCity, a new dataset for omnipotent city understanding from multi-level and multi-view images. More precisely, OmniCity contains multi-view satellite images as well as street-level panorama and monoview images, constituting over 100K pixel-wise annotated images that are well-aligned and collected from 25K geolocations in New York City. To alleviate the substantial pixel-wise annotation efforts, we propose an efficient street-view image annotation pipeline that leverages the existing label maps of satellite view and the transformation relations between different views (satellite, panorama, and mono-view). With the new OmniCity dataset, we provide benchmarks for a variety of tasks including building footprint extraction, height estimation, and building plane/instance/fine-grained segmentation. Compared with existing multi-level and multi-view benchmarks, OmniCity contains a larger number of images with richer annotation types and more views, provides more benchmark results of state-of-the-art models, and introduces a new task for fine-grained building instance segmentation on street-level panorama images. Moreover, OmniCity provides new problem settings for existing tasks, such as cross-view image matching, synthesis, segmentation, detection, etc., and facilitates the developing of new methods for large-scale city understanding, reconstruction, and simulation. The OmniCity dataset as well as the benchmarks will be released at https://***/omnicity/.

关键词： Photogrammetry and remote sensing

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