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
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
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
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
文摘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.
文摘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.
基金co-supported by the National Natural Science Foundations of China(Nos.62175111 and 62001234)。
文摘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.
基金partially supported by National Natural Science Foundation of China(No.61872317)Face Unity Technology。
文摘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.
