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A Cross-View Model for Tourism Demand Forecasting with Artificial Intelligence Method
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作者 Siming Han Yanhui Guo +2 位作者 Han Cao Qian Feng Yifei Li 《国际计算机前沿大会会议论文集》 2017年第1期144-146,共3页
Forecasting always plays a vital role in modern economic and industrial fields,and tourism demand forecasting is an important part of intelligent tourism.This paper proposes a simple method for data modeling and a com... Forecasting always plays a vital role in modern economic and industrial fields,and tourism demand forecasting is an important part of intelligent tourism.This paper proposes a simple method for data modeling and a combined cross-view model,which is easy to implement but very effective.The method presented in this paper is commonly used for BPNN and SVR algorithms.A real tourism data set of Small Wild Goose Pagoda is used to verify the feasibility of the proposed method,with the analysis of the impact of year,season,and week on tourism demand forecasting.Comparative experiments suggest that the proposed model shows better accuracy than contrast methods. 展开更多
关键词 cross-view BPNN SVR ARIMA
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Multi-scale attention encoder for street-to-aerial image geo-localization 被引量:2
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作者 Songlian Li Zhigang Tu +1 位作者 Yujin Chen Tan Yu 《CAAI Transactions on Intelligence Technology》 SCIE EI 2023年第1期166-176,共11页
The goal of street-to-aerial cross-view image geo-localization is to determine the location of the query street-view image by retrieving the aerial-view image from the same place.The drastic viewpoint and appearance g... The goal of street-to-aerial cross-view image geo-localization is to determine the location of the query street-view image by retrieving the aerial-view image from the same place.The drastic viewpoint and appearance gap between the aerial-view and the street-view images brings a huge challenge against this task.In this paper,we propose a novel multiscale attention encoder to capture the multiscale contextual information of the aerial/street-view images.To bridge the domain gap between these two view images,we first use an inverse polar transform to make the street-view images approximately aligned with the aerial-view images.Then,the explored multiscale attention encoder is applied to convert the image into feature representation with the guidance of the learnt multiscale information.Finally,we propose a novel global mining strategy to enable the network to pay more attention to hard negative exemplars.Experiments on standard benchmark datasets show that our approach obtains 81.39%top-1 recall rate on the CVUSA dataset and 71.52%on the CVACT dataset,achieving the state-of-the-art performance and outperforming most of the existing methods significantly. 展开更多
关键词 global mining strategy image geo-localization multiscale attention encoder street-to-aerial cross-view
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On Robust Cross-view Consistency in Self-supervised Monocular Depth Estimation
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作者 Haimei Zhao Jing Zhang +2 位作者 Zhuo Chen Bo Yuan Dacheng Tao 《Machine Intelligence Research》 EI CSCD 2024年第3期495-513,共19页
Remarkable progress has been made in self-supervised monocular depth estimation (SS-MDE) by exploring cross-view consistency, e.g., photometric consistency and 3D point cloud consistency. However, they are very vulner... Remarkable progress has been made in self-supervised monocular depth estimation (SS-MDE) by exploring cross-view consistency, e.g., photometric consistency and 3D point cloud consistency. However, they are very vulnerable to illumination variance, occlusions, texture-less regions, as well as moving objects, making them not robust enough to deal with various scenes. To address this challenge, we study two kinds of robust cross-view consistency in this paper. Firstly, the spatial offset field between adjacent frames is obtained by reconstructing the reference frame from its neighbors via deformable alignment, which is used to align the temporal depth features via a depth feature alignment (DFA) loss. Secondly, the 3D point clouds of each reference frame and its nearby frames are calculated and transformed into voxel space, where the point density in each voxel is calculated and aligned via a voxel density alignment (VDA) loss. In this way, we exploit the temporal coherence in both depth feature space and 3D voxel space for SS-MDE, shifting the “point-to-point” alignment paradigm to the “region-to-region” one. Compared with the photometric consistency loss as well as the rigid point cloud alignment loss, the proposed DFA and VDA losses are more robust owing to the strong representation power of deep features as well as the high tolerance of voxel density to the aforementioned challenges. Experimental results on several outdoor benchmarks show that our method outperforms current state-of-the-art techniques. Extensive ablation study and analysis validate the effectiveness of the proposed losses, especially in challenging scenes. The code and models are available at https://github.com/sunnyHelen/RCVC-depth. 展开更多
关键词 3D vision depth estimation cross-view consistency self-supervised learning monocular perception
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永久型Windows Rootkit检测技术 被引量:1
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作者 王全民 于佳耕 +1 位作者 赵彤 韩红英 《计算机工程》 CAS CSCD 北大核心 2008年第18期70-71,74,共3页
永久型Rootkit可以长期隐秘在系统中,并隐藏恶意代码,威胁计算机的安全。该文应用cross-view方法构建监控系统,采用文件系统过滤驱动与钩挂系统服务分析系统行为,判定系统是否已被装入永久型WindowsRootkit,并完成对经典Rootkit-hackerd... 永久型Rootkit可以长期隐秘在系统中,并隐藏恶意代码,威胁计算机的安全。该文应用cross-view方法构建监控系统,采用文件系统过滤驱动与钩挂系统服务分析系统行为,判定系统是否已被装入永久型WindowsRootkit,并完成对经典Rootkit-hackerdefender及它所保护的恶意程序的检测。由于该检测技术使用底层驱动监测,不依赖特征码,因此对内核级和将来出现的Rootkit具有良好的检测效果。 展开更多
关键词 监控系统 永久型Rootkit cross-view方法
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Research on the Pedestrian Re-Identification Method Based on Local Features and Gait Energy Images 被引量:3
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作者 Xinliang Tang Xing Sun +3 位作者 Zhenzhou Wang Pingping Yu Ning Cao Yunfeng Xu 《Computers, Materials & Continua》 SCIE EI 2020年第8期1185-1198,共14页
The appearance of pedestrians can vary greatly from image to image,and different pedestrians may look similar in a given image.Such similarities and variabilities in the appearance and clothing of individuals make the... The appearance of pedestrians can vary greatly from image to image,and different pedestrians may look similar in a given image.Such similarities and variabilities in the appearance and clothing of individuals make the task of pedestrian re-identification very challenging.Here,a pedestrian re-identification method based on the fusion of local features and gait energy image(GEI)features is proposed.In this method,the human body is divided into four regions according to joint points.The color and texture of each region of the human body are extracted as local features,and GEI features of the pedestrian gait are also obtained.These features are then fused with the local and GEI features of the person.Independent distance measure learning using the cross-view quadratic discriminant analysis(XQDA)method is used to obtain the similarity of the metric function of the image pairs,and the final similarity is acquired by weight matching.Evaluation of experimental results by cumulative matching characteristic(CMC)curves reveals that,after fusion of local and GEI features,the pedestrian re-identification effect is improved compared with existing methods and is notably better than the recognition rate of pedestrian re-identification with a single feature. 展开更多
关键词 Local features gait energy image WEIGHT independent distance metric cross-view quadratic discriminant analysis
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Target localization based on cross-view matching between UAV and satellite 被引量:3
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作者 Kan REN Lei DING +2 位作者 Minjie WAN Guohua GU Qian CHEN 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2022年第9期333-341,共9页
Matching remote sensing images taken by an unmanned aerial vehicle(UAV) with satellite remote sensing images with geolocation information. Thus, the specific geographic location of the target object captured by the UA... Matching remote sensing images taken by an unmanned aerial vehicle(UAV) with satellite remote sensing images with geolocation information. Thus, the specific geographic location of the target object captured by the UAV is determined. Its main challenge is the considerable differences in the visual content of remote sensing images acquired by satellites and UAVs, such as dramatic changes in viewpoint, unknown orientations, etc. Much of the previous work has focused on image matching of homologous data. To overcome the difficulties caused by the difference between these two data modes and maintain robustness in visual positioning, a quality-aware template matching method based on scale-adaptive deep convolutional features is proposed by deeply mining their common features. The template size feature map and the reference image feature map are first obtained. The two feature maps obtained are used to measure the similarity. Finally, a heat map representing the probability of matching is generated to determine the best match in the reference image. The method is applied to the latest UAV-based geolocation dataset(University-1652 dataset) and the real-scene campus data we collected with UAVs. The experimental results demonstrate the effectiveness and superiority of the method. 展开更多
关键词 cross-view image matching SATELLITE Target localization Template matching Unmanned Aerial Vehicle(UAV)
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3D hypothesis clustering for cross-view matching in multiperson motion capture 被引量:1
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作者 Miaopeng Li Zimeng Zhou Xinguo Liu 《Computational Visual Media》 CSCD 2020年第2期147-156,共10页
We present a multiview method for markerless motion capture of multiple people. The main challenge in this problem is to determine crossview correspondences for the 2 D joints in the presence of noise. We propose a 3 ... We present a multiview method for markerless motion capture of multiple people. The main challenge in this problem is to determine crossview correspondences for the 2 D joints in the presence of noise. We propose a 3 D hypothesis clustering technique to solve this problem. The core idea is to transform joint matching in 2 D space into a clustering problem in a 3 D hypothesis space. In this way, evidence from photometric appearance, multiview geometry, and bone length can be integrated to solve the clustering problem efficiently and robustly. Each cluster encodes a set of matched 2 D joints for the same person across different views, from which the 3 D joints can be effectively inferred. We then assemble the inferred 3 D joints to form full-body skeletons for all persons in a bottom–up way. Our experiments demonstrate the robustness of our approach even in challenging cases with heavy occlusion,closely interacting people, and few cameras. We have evaluated our method on many datasets, and our results show that it has significantly lower estimation errors than many state-of-the-art methods. 展开更多
关键词 multi-person motion capture cross-view matching CLUSTERING human pose estimation
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