In recent years,many visual positioning algorithms have been proposed based on computer vision and they have achieved good results.However,these algorithms have a single function,cannot perceive the environment,and ha...In recent years,many visual positioning algorithms have been proposed based on computer vision and they have achieved good results.However,these algorithms have a single function,cannot perceive the environment,and have poor versatility,and there is a certain mismatch phenomenon,which affects the positioning accuracy.Therefore,this paper proposes a location algorithm that combines a target recognition algorithm with a depth feature matching algorithm to solve the problem of unmanned aerial vehicle(UAV)environment perception and multi-modal image-matching fusion location.This algorithm was based on the single-shot object detector based on multi-level feature pyramid network(M2Det)algorithm and replaced the original visual geometry group(VGG)feature extraction network with the ResNet-101 network to improve the feature extraction capability of the network model.By introducing a depth feature matching algorithm,the algorithm shares neural network weights and realizes the design of UAV target recognition and a multi-modal image-matching fusion positioning algorithm.When the reference image and the real-time image were mismatched,the dynamic adaptive proportional constraint and the random sample consensus consistency algorithm(DAPC-RANSAC)were used to optimize the matching results to improve the correct matching efficiency of the target.Using the multi-modal registration data set,the proposed algorithm was compared and analyzed to verify its superiority and feasibility.The results show that the algorithm proposed in this paper can effectively deal with the matching between multi-modal images(visible image–infrared image,infrared image–satellite image,visible image–satellite image),and the contrast,scale,brightness,ambiguity deformation,and other changes had good stability and robustness.Finally,the effectiveness and practicability of the algorithm proposed in this paper were verified in an aerial test scene of an S1000 sixrotor UAV.展开更多
To solve the heterogeneous image scene matching problem, a non-linear pre-processing method for the original images before intensity-based correlation is proposed. The result shows that the proper matching probability...To solve the heterogeneous image scene matching problem, a non-linear pre-processing method for the original images before intensity-based correlation is proposed. The result shows that the proper matching probability is raised greatly. Especially for the low S/N image pairs, the effect is more remarkable.展开更多
Performance analysis is very important in the study and design of scene matching algorithm. Based on the analysis of the common performance parameters, robustness of scene matching algorithm is defined, including the ...Performance analysis is very important in the study and design of scene matching algorithm. Based on the analysis of the common performance parameters, robustness of scene matching algorithm is defined, including the definitions of robust stability and robust performance, and the corresponding evaluation parameters matching margin and matching adaptability are given. With application of these robustness parameters on 8 scene matching algorithms, quantitative analysis results of algorithm robustness are obtained. The paper provides an important theoretical reference to the performance evaluation of scene matching algorithm.展开更多
Time series data plays a crucial role in intelligent transportation systems.Traffic flow forecasting represents a precise estimation of future traffic flow within a specific region and time interval.Existing approache...Time series data plays a crucial role in intelligent transportation systems.Traffic flow forecasting represents a precise estimation of future traffic flow within a specific region and time interval.Existing approaches,including sequence periodic,regression,and deep learning models,have shown promising results in short-term series forecasting.However,forecasting scenarios specifically focused on holiday traffic flow present unique challenges,such as distinct traffic patterns during vacations and the increased demand for long-term forecastings.Consequently,the effectiveness of existing methods diminishes in such scenarios.Therefore,we propose a novel longterm forecasting model based on scene matching and embedding fusion representation to forecast long-term holiday traffic flow.Our model comprises three components:the similar scene matching module,responsible for extracting Similar Scene Features;the long-short term representation fusion module,which integrates scenario embeddings;and a simple fully connected layer at the head for making the final forecasting.Experimental results on real datasets demonstrate that our model outperforms other methods,particularly in medium and long-term forecasting scenarios.展开更多
A new scene recognition system was presented based on fuzzy logic and hidden Markov model(HMM)that can be applied in mine rescue robot localization during emergencies.The system uses monocular camera to acquire omni-d...A new scene recognition system was presented based on fuzzy logic and hidden Markov model(HMM)that can be applied in mine rescue robot localization during emergencies.The system uses monocular camera to acquire omni-directional images of the mine environment where the robot locates.By adopting center-surround difference method,the salient local image regions are extracted from the images as natural landmarks.These landmarks are organized by using HMM to represent the scene where the robot is,and fuzzy logic strategy is used to match the scene and landmark.By this way,the localization problem,which is the scene recognition problem in the system,can be converted into the evaluation problem of HMM.The contributions of these skills make the system have the ability to deal with changes in scale,2D rotation and viewpoint.The results of experiments also prove that the system has higher ratio of recognition and localization in both static and dynamic mine environments.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant 62276274in part by the Natural Science Foundation of Shaanxi Province under Grant 2020JM-537,and in part by the Aeronautical Science Fund under Grant 201851U8012(corresponding author:Xiaogang Yang).
文摘In recent years,many visual positioning algorithms have been proposed based on computer vision and they have achieved good results.However,these algorithms have a single function,cannot perceive the environment,and have poor versatility,and there is a certain mismatch phenomenon,which affects the positioning accuracy.Therefore,this paper proposes a location algorithm that combines a target recognition algorithm with a depth feature matching algorithm to solve the problem of unmanned aerial vehicle(UAV)environment perception and multi-modal image-matching fusion location.This algorithm was based on the single-shot object detector based on multi-level feature pyramid network(M2Det)algorithm and replaced the original visual geometry group(VGG)feature extraction network with the ResNet-101 network to improve the feature extraction capability of the network model.By introducing a depth feature matching algorithm,the algorithm shares neural network weights and realizes the design of UAV target recognition and a multi-modal image-matching fusion positioning algorithm.When the reference image and the real-time image were mismatched,the dynamic adaptive proportional constraint and the random sample consensus consistency algorithm(DAPC-RANSAC)were used to optimize the matching results to improve the correct matching efficiency of the target.Using the multi-modal registration data set,the proposed algorithm was compared and analyzed to verify its superiority and feasibility.The results show that the algorithm proposed in this paper can effectively deal with the matching between multi-modal images(visible image–infrared image,infrared image–satellite image,visible image–satellite image),and the contrast,scale,brightness,ambiguity deformation,and other changes had good stability and robustness.Finally,the effectiveness and practicability of the algorithm proposed in this paper were verified in an aerial test scene of an S1000 sixrotor UAV.
文摘To solve the heterogeneous image scene matching problem, a non-linear pre-processing method for the original images before intensity-based correlation is proposed. The result shows that the proper matching probability is raised greatly. Especially for the low S/N image pairs, the effect is more remarkable.
文摘Performance analysis is very important in the study and design of scene matching algorithm. Based on the analysis of the common performance parameters, robustness of scene matching algorithm is defined, including the definitions of robust stability and robust performance, and the corresponding evaluation parameters matching margin and matching adaptability are given. With application of these robustness parameters on 8 scene matching algorithms, quantitative analysis results of algorithm robustness are obtained. The paper provides an important theoretical reference to the performance evaluation of scene matching algorithm.
基金funded by the Natural Science Foundation of Zhejiang Province of China under Grant (No.LY21F020003)Zhejiang Science and Technology Plan Project (No.2021C02060)the Scientific Research Foundation of Hangzhou City University (No.X-202206).
文摘Time series data plays a crucial role in intelligent transportation systems.Traffic flow forecasting represents a precise estimation of future traffic flow within a specific region and time interval.Existing approaches,including sequence periodic,regression,and deep learning models,have shown promising results in short-term series forecasting.However,forecasting scenarios specifically focused on holiday traffic flow present unique challenges,such as distinct traffic patterns during vacations and the increased demand for long-term forecastings.Consequently,the effectiveness of existing methods diminishes in such scenarios.Therefore,we propose a novel longterm forecasting model based on scene matching and embedding fusion representation to forecast long-term holiday traffic flow.Our model comprises three components:the similar scene matching module,responsible for extracting Similar Scene Features;the long-short term representation fusion module,which integrates scenario embeddings;and a simple fully connected layer at the head for making the final forecasting.Experimental results on real datasets demonstrate that our model outperforms other methods,particularly in medium and long-term forecasting scenarios.
基金Project(60234030)supported by the National Natural Science Foundation of ChinaProject(A1420060159)supported by the BasicResearch Program of the 11th Five-Year-Plan of China
文摘A new scene recognition system was presented based on fuzzy logic and hidden Markov model(HMM)that can be applied in mine rescue robot localization during emergencies.The system uses monocular camera to acquire omni-directional images of the mine environment where the robot locates.By adopting center-surround difference method,the salient local image regions are extracted from the images as natural landmarks.These landmarks are organized by using HMM to represent the scene where the robot is,and fuzzy logic strategy is used to match the scene and landmark.By this way,the localization problem,which is the scene recognition problem in the system,can be converted into the evaluation problem of HMM.The contributions of these skills make the system have the ability to deal with changes in scale,2D rotation and viewpoint.The results of experiments also prove that the system has higher ratio of recognition and localization in both static and dynamic mine environments.
