It is difficult to extract deep semantic features for English composition scoring methods based on artificial features, and it is difficult for English composition scoring methods based on neural networks to extract shallow features such as the number of words, resulting in the limitations of different composition scoring methods. Based on existing research results, this paper proposes an English composition scoring method that combines artificial feature extraction methods and deep learning methods. This method uses artificially designed features to extract shallow features at the word and sentence levels in the composition, draws on existing methods to extract semantic features of the composition, and performs regression calculations on the deep features and shallow features to obtain the total score of the composition. The experiment uses the Pearson evaluation index to measure the correlation between the predicted total score of the essay and the true total score under the combination method. The experiment shows that compared with the average results of 0.747 and 0.645 of baseline models such as BiLSTM and RNN, the algorithm proposed in this article is respectively improvements are 0.068 and 0.17, which proves the effectiveness of the method proposed in this paper.
{"title":"Research on automatic scoring algorithm for English composition based on machine learning","authors":"Hui Li","doi":"10.1117/12.3014482","DOIUrl":"https://doi.org/10.1117/12.3014482","url":null,"abstract":"It is difficult to extract deep semantic features for English composition scoring methods based on artificial features, and it is difficult for English composition scoring methods based on neural networks to extract shallow features such as the number of words, resulting in the limitations of different composition scoring methods. Based on existing research results, this paper proposes an English composition scoring method that combines artificial feature extraction methods and deep learning methods. This method uses artificially designed features to extract shallow features at the word and sentence levels in the composition, draws on existing methods to extract semantic features of the composition, and performs regression calculations on the deep features and shallow features to obtain the total score of the composition. The experiment uses the Pearson evaluation index to measure the correlation between the predicted total score of the essay and the true total score under the combination method. The experiment shows that compared with the average results of 0.747 and 0.645 of baseline models such as BiLSTM and RNN, the algorithm proposed in this article is respectively improvements are 0.068 and 0.17, which proves the effectiveness of the method proposed in this paper.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":"20 6","pages":"129690T - 129690T-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139640403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Danqing Zhao, Shuyi Xin, Lechen Liu, Yihan Sun, Anqi Du
The development of the Metaverse nowadays has sparked widespread emotions among researchers, and correspondingly, many technologies have been derived to improve the human's sense of reality in the Metaverse. Especially, Extended Reality (XR), as an indispensable and important technology and research direction in the study of the metaverse, aims to bring seamless transformation between the virtual world and the real-world immersion to the experiential world. However, the technology we currently lack is the ability to simultaneously separate, classify, and locate dynamic human sound information to enhance human sound perception in complex noise environments. This article proposes a framework that utilizes FCNN for separation, algebraic models for positioning to obtain estimated distances, and SVM for classification. The dataset is built to simulates distance-related changes with accurate ground truth labels. The results show that our method can effectively separate, separate, and locate mixed sound data, providing users with comprehensive information about the content, gender, and distance of the speaking object in complex sound environments, enhancing their immersive experience and perception ability. Our innovation lies in the combination of three audio processing technologies and the framework proposed may well inspire future work on related topics.
{"title":"Enhancing audio perception in augmented reality: a dynamic vocal information processing framework","authors":"Danqing Zhao, Shuyi Xin, Lechen Liu, Yihan Sun, Anqi Du","doi":"10.1117/12.3014440","DOIUrl":"https://doi.org/10.1117/12.3014440","url":null,"abstract":"The development of the Metaverse nowadays has sparked widespread emotions among researchers, and correspondingly, many technologies have been derived to improve the human's sense of reality in the Metaverse. Especially, Extended Reality (XR), as an indispensable and important technology and research direction in the study of the metaverse, aims to bring seamless transformation between the virtual world and the real-world immersion to the experiential world. However, the technology we currently lack is the ability to simultaneously separate, classify, and locate dynamic human sound information to enhance human sound perception in complex noise environments. This article proposes a framework that utilizes FCNN for separation, algebraic models for positioning to obtain estimated distances, and SVM for classification. The dataset is built to simulates distance-related changes with accurate ground truth labels. The results show that our method can effectively separate, separate, and locate mixed sound data, providing users with comprehensive information about the content, gender, and distance of the speaking object in complex sound environments, enhancing their immersive experience and perception ability. Our innovation lies in the combination of three audio processing technologies and the framework proposed may well inspire future work on related topics.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":" 22","pages":"129691Z - 129691Z-9"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139640520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In response to the thin nature of hot rolled steel plates and strips, the vast majority of which are surface defects that can easily lead to production accidents, and limited by the challenges of insufficient datasets and a large amount of unlabeled data, this paper proposes a comparative learning method to solve the above problems. In terms of methods, a dual data augmentation strategy is adopted. Firstly, the original image is data enhanced through manual processing, and CycleGAN is introduced for style transfer to enrich the dataset. Then, ResNet152 network is used for feature extraction, and several comparative learning methods are applied to observe the accuracy of hot rolled strip defect detection. In the end, the improved comparative learning method in this article successfully improved the accuracy of surface defect classification for hot rolled strip steel. Through this research, we are committed to providing more reliable quality control methods for industrial production and reducing the risk of production accidents.
{"title":"Research on surface defect classification method of hot rolled strip steel based on comparative learning","authors":"Xingshuai Zang, Shengnan Zhang, Yu He","doi":"10.1117/12.3014479","DOIUrl":"https://doi.org/10.1117/12.3014479","url":null,"abstract":"In response to the thin nature of hot rolled steel plates and strips, the vast majority of which are surface defects that can easily lead to production accidents, and limited by the challenges of insufficient datasets and a large amount of unlabeled data, this paper proposes a comparative learning method to solve the above problems. In terms of methods, a dual data augmentation strategy is adopted. Firstly, the original image is data enhanced through manual processing, and CycleGAN is introduced for style transfer to enrich the dataset. Then, ResNet152 network is used for feature extraction, and several comparative learning methods are applied to observe the accuracy of hot rolled strip defect detection. In the end, the improved comparative learning method in this article successfully improved the accuracy of surface defect classification for hot rolled strip steel. Through this research, we are committed to providing more reliable quality control methods for industrial production and reducing the risk of production accidents.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":"37 3","pages":"129691K - 129691K-5"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we research the multi-objective vehicle routing problem with time windows under uncertainty. For solving it efficiently, the robust multi-objective particle swarm optimization incorporates the simulated annealing algorithm is proposed. The new algorithm aims to improve the local search abilities of particles. Experimental results show that the proposed algorithm outperforms the traditional the robust multi-objective particle swarm optimization algorithm on the selected problem sets as the uncertain interference intensity increases.
{"title":"Multi-objective vehicle routing problem with time windows under uncertain conditions","authors":"jiashuo guo, Yuxin Liu","doi":"10.1117/12.3014402","DOIUrl":"https://doi.org/10.1117/12.3014402","url":null,"abstract":"In this paper, we research the multi-objective vehicle routing problem with time windows under uncertainty. For solving it efficiently, the robust multi-objective particle swarm optimization incorporates the simulated annealing algorithm is proposed. The new algorithm aims to improve the local search abilities of particles. Experimental results show that the proposed algorithm outperforms the traditional the robust multi-objective particle swarm optimization algorithm on the selected problem sets as the uncertain interference intensity increases.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":"18 4","pages":"129692C - 129692C-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The rapid development of the cross-border e-commerce market has led to an increase in logistics complexity, and intelligent monitoring and early warning systems are needed to meet the challenges. The objective of this study is to design and implement a cross-border e-commerce logistics monitoring and early warning system based on improved genetic algorithms to enhance the reliability of transportation quality. The system collects data related to cross-border e-commerce logistics transportation quality, analyzes and optimizes the improved genetic algorithm in one system, and uses the improved genetic algorithm for decision-making and planning. The system has a real-time monitoring function to discover potential transportation quality problems and conduct predictive analysis to identify the min advance for timely warning. The system can provide cross-border e-commerce enterprises with more efficient logistics and transportation quality management, reduce costs and improve customer satisfaction. It helps enterprises to cope with logistics challenges, provide more reliable services, and promote the continuous development and prosperity of cross-border e-commerce.
{"title":"Design and realization of cross-border e-commerce logistics intelligent monitoring and early warning system based on improved genetic algorithm","authors":"Fei Lei, Zicen Liao, Mingxiu Huang, Hui Tian","doi":"10.1117/12.3014648","DOIUrl":"https://doi.org/10.1117/12.3014648","url":null,"abstract":"The rapid development of the cross-border e-commerce market has led to an increase in logistics complexity, and intelligent monitoring and early warning systems are needed to meet the challenges. The objective of this study is to design and implement a cross-border e-commerce logistics monitoring and early warning system based on improved genetic algorithms to enhance the reliability of transportation quality. The system collects data related to cross-border e-commerce logistics transportation quality, analyzes and optimizes the improved genetic algorithm in one system, and uses the improved genetic algorithm for decision-making and planning. The system has a real-time monitoring function to discover potential transportation quality problems and conduct predictive analysis to identify the min advance for timely warning. The system can provide cross-border e-commerce enterprises with more efficient logistics and transportation quality management, reduce costs and improve customer satisfaction. It helps enterprises to cope with logistics challenges, provide more reliable services, and promote the continuous development and prosperity of cross-border e-commerce.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":"58 1","pages":"129690W - 129690W-4"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Synthetic Aperture Radar (SAR) is capable of producing high-resolution complex-valued pictures, which have extensive applications in both civil and military domains. Among these applications, SAR electronic countermeasures currently represent a prominent area of research interest. Presently, within the radar electronic countermeasures, there exists a diminishing disparity among the features of real and false targets, rendering the detection of jamming increasingly challenging. This paper examines the phase of SAR images and presents a method for identifying SAR jamming regions based on phase features. The initial step involves organizing the cluttered phase information into neighborhood phase differences. Subsequently, this information is coupled with the amplitude to obtain the weighted phase difference. This metric effectively captures the extent of phase distortion resulting from jamming. The findings from the simulation experiment demonstrate that the proposed feature and method are capable of accurately identifying and filtering out the jamming region in SAR pictures. Furthermore, it demonstrates the prospection of phase within the SAR image interpretation and electronic countermeasures.
{"title":"Jamming detection based on phase feature for SAR images","authors":"Haoyu Zhang, Sinong Quan, Shiqi Xing, Yitao Liu","doi":"10.1117/12.3014617","DOIUrl":"https://doi.org/10.1117/12.3014617","url":null,"abstract":"Synthetic Aperture Radar (SAR) is capable of producing high-resolution complex-valued pictures, which have extensive applications in both civil and military domains. Among these applications, SAR electronic countermeasures currently represent a prominent area of research interest. Presently, within the radar electronic countermeasures, there exists a diminishing disparity among the features of real and false targets, rendering the detection of jamming increasingly challenging. This paper examines the phase of SAR images and presents a method for identifying SAR jamming regions based on phase features. The initial step involves organizing the cluttered phase information into neighborhood phase differences. Subsequently, this information is coupled with the amplitude to obtain the weighted phase difference. This metric effectively captures the extent of phase distortion resulting from jamming. The findings from the simulation experiment demonstrate that the proposed feature and method are capable of accurately identifying and filtering out the jamming region in SAR pictures. Furthermore, it demonstrates the prospection of phase within the SAR image interpretation and electronic countermeasures.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":"27 1","pages":"129691X - 129691X-5"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Based on the color model, this paper investigates the green glass in Cantonese colored windows, aiming to establish the standard value of the green color of Cantonese colored windows, and to provide relevant data and suggestions to protect the design concept of Cantonese architectural decoration and regulate the use of color in colored windows. By collecting samples, with the help of image processing and color analysis software, the green glass was positioned and analyzed in both RGB and LAB color models. It is found that in the RGB color model, the threshold intervals of green glass are mainly concentrated in the Forest Green and Green regions; in the LAB color model, the threshold intervals of the two-color channels of green glass are mainly distributed in the range of medium and low saturation. This study provides digitalized standard values and reference data for the green of the Cantonese colored windows, which helps to maintain the design style of traditional architecture and promote the development and application of colored windows.
{"title":"Research on Green glass of Cantonese colored windows based on color model","authors":"Xiaoqing Wang, Ying Du","doi":"10.1117/12.3014495","DOIUrl":"https://doi.org/10.1117/12.3014495","url":null,"abstract":"Based on the color model, this paper investigates the green glass in Cantonese colored windows, aiming to establish the standard value of the green color of Cantonese colored windows, and to provide relevant data and suggestions to protect the design concept of Cantonese architectural decoration and regulate the use of color in colored windows. By collecting samples, with the help of image processing and color analysis software, the green glass was positioned and analyzed in both RGB and LAB color models. It is found that in the RGB color model, the threshold intervals of green glass are mainly concentrated in the Forest Green and Green regions; in the LAB color model, the threshold intervals of the two-color channels of green glass are mainly distributed in the range of medium and low saturation. This study provides digitalized standard values and reference data for the green of the Cantonese colored windows, which helps to maintain the design style of traditional architecture and promote the development and application of colored windows.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":"15 3","pages":"129691C - 129691C-8"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jinxiang Feng, Jingyang Li, Yufeng Zhang, H. Baoyin
Dung Beetle Optimizer(DBO) is an effective metaheuristic algorithm proposed in 2022. But at the same time, DBO also suffers from a local-global imbalance in the exploration process, tends to fall into local optimization and exploitability needs to be further improved, etc. Therefore, we propose an improved DBO algorithm to address these shortcomings and named it CDBO. Firstly, Tent chaotic mapping can be used for the purpose of initializing the population, improving the quality of initial solutions, promoting the enhancement of population variety, and augmenting the global search capability of the algorithm. Secondly, introducing dynamic weighting factors enables the algorithm to fully search for local areas while also taking into account global exploration. To assess the effectiveness of CDBO, a total of 12 benchmark test functions were utilized to evaluate the performance of this algorithm, wherein CDBO was compared with other widely recognized metaheuristic algorithms. The results showed that CDBO had improved search accuracy and convergence speed. Finally, CDBO was applied to airfoil optimization problem, verifying the feasibility of applying CDBO to practical engineering problems.
{"title":"An improved dung beetle optimizer","authors":"Jinxiang Feng, Jingyang Li, Yufeng Zhang, H. Baoyin","doi":"10.1117/12.3014472","DOIUrl":"https://doi.org/10.1117/12.3014472","url":null,"abstract":"Dung Beetle Optimizer(DBO) is an effective metaheuristic algorithm proposed in 2022. But at the same time, DBO also suffers from a local-global imbalance in the exploration process, tends to fall into local optimization and exploitability needs to be further improved, etc. Therefore, we propose an improved DBO algorithm to address these shortcomings and named it CDBO. Firstly, Tent chaotic mapping can be used for the purpose of initializing the population, improving the quality of initial solutions, promoting the enhancement of population variety, and augmenting the global search capability of the algorithm. Secondly, introducing dynamic weighting factors enables the algorithm to fully search for local areas while also taking into account global exploration. To assess the effectiveness of CDBO, a total of 12 benchmark test functions were utilized to evaluate the performance of this algorithm, wherein CDBO was compared with other widely recognized metaheuristic algorithms. The results showed that CDBO had improved search accuracy and convergence speed. Finally, CDBO was applied to airfoil optimization problem, verifying the feasibility of applying CDBO to practical engineering problems.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":"9 2","pages":"129692V - 129692V-9"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aiming at the current problem of unsatisfactory vehicle detection in complex scenes, an improved vehicle target detection network model is proposed. First, Res2Net residual network is fused in SCP, and the CSP_R structure is proposed, so that the model can extract deeper feature information and strengthen the ability to characterize small-scale targets; the attention mechanism is introduced, and the C3_CBAM module is designed to strengthen the attention to the detection targets while avoiding the increase of the model's computational volume; the loss function of the MPDIoU regression optimization is introduced, and the loss function is optimized by combining the prediction frame with the real frame length, width and area loss, and quantitative indicators to improve the convergence speed and robustness of the model. Finally, the model is validated on the SODA10M dataset, and the experimental results show that the model detection speed reaches 32 frames per second. The average detection accuracy reaches 83.7%, which is an improvement of 7.8 percentage points compared with YOLOV5s.
{"title":"Research on target detection algorithm based on vehicle detection","authors":"Yanguo Huang, Zehao Rao, Luo Li","doi":"10.1117/12.3014382","DOIUrl":"https://doi.org/10.1117/12.3014382","url":null,"abstract":"Aiming at the current problem of unsatisfactory vehicle detection in complex scenes, an improved vehicle target detection network model is proposed. First, Res2Net residual network is fused in SCP, and the CSP_R structure is proposed, so that the model can extract deeper feature information and strengthen the ability to characterize small-scale targets; the attention mechanism is introduced, and the C3_CBAM module is designed to strengthen the attention to the detection targets while avoiding the increase of the model's computational volume; the loss function of the MPDIoU regression optimization is introduced, and the loss function is optimized by combining the prediction frame with the real frame length, width and area loss, and quantitative indicators to improve the convergence speed and robustness of the model. Finally, the model is validated on the SODA10M dataset, and the experimental results show that the model detection speed reaches 32 frames per second. The average detection accuracy reaches 83.7%, which is an improvement of 7.8 percentage points compared with YOLOV5s.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":"114 1","pages":"129690K - 129690K-7"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jianjun Peng, Jialei Zhai, Xiang Jin, Chengshuang Hu, Zaigang Li
As the global pandemic gradually eases and the aviation transport industry continues to experience steady growth, highdensity flight operations are becoming the new normal. The intelligentization of flight support processes is a crucial avenue for enhancing both the safety and efficiency of flight operations. With the advancement of computer vision technology, video-based object tracking has shown significant potential in the context of flight support processes. However, in real airport environments, object tracking often encounters challenges such as occlusion, scale variations, rotation, and changes in lighting conditions, leading to a decrease in tracking accuracy and even target loss. In this paper, our focus is on overcoming tracking failures caused by occlusion, deformation, and lighting variations. We have conducted the following work, taking into consideration the unique characteristics of airport environments and the specific requirements of flight support processes: (i) We utilized features at three levels, namely, Histogram of Oriented Gradient (HOG), Color Names, and Convolutional Neural Networks (CNN), to describe the texture, color, and high-level semantics of video images, respectively. (ii) We employed a multi-feature fusion approach using a trilinear interpolation function to integrate information from various sources. (iii) We implemented improved ECO algorithms for the tracking of moving objects in the airport environment. Finally, we validated this object tracking system using real surveillance videos from the airport. Experimental results have demonstrated the effectiveness and practicality of the method under challenging conditions.
{"title":"The application of target tracking algorithm in intelligent video system to flight support","authors":"Jianjun Peng, Jialei Zhai, Xiang Jin, Chengshuang Hu, Zaigang Li","doi":"10.1117/12.3014375","DOIUrl":"https://doi.org/10.1117/12.3014375","url":null,"abstract":"As the global pandemic gradually eases and the aviation transport industry continues to experience steady growth, highdensity flight operations are becoming the new normal. The intelligentization of flight support processes is a crucial avenue for enhancing both the safety and efficiency of flight operations. With the advancement of computer vision technology, video-based object tracking has shown significant potential in the context of flight support processes. However, in real airport environments, object tracking often encounters challenges such as occlusion, scale variations, rotation, and changes in lighting conditions, leading to a decrease in tracking accuracy and even target loss. In this paper, our focus is on overcoming tracking failures caused by occlusion, deformation, and lighting variations. We have conducted the following work, taking into consideration the unique characteristics of airport environments and the specific requirements of flight support processes: (i) We utilized features at three levels, namely, Histogram of Oriented Gradient (HOG), Color Names, and Convolutional Neural Networks (CNN), to describe the texture, color, and high-level semantics of video images, respectively. (ii) We employed a multi-feature fusion approach using a trilinear interpolation function to integrate information from various sources. (iii) We implemented improved ECO algorithms for the tracking of moving objects in the airport environment. Finally, we validated this object tracking system using real surveillance videos from the airport. Experimental results have demonstrated the effectiveness and practicality of the method under challenging conditions.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":"96 3","pages":"129690Q - 129690Q-8"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：