International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)最新文献_第8页

Jamming detection based on phase feature for SAR images 基于相位特征的合成孔径雷达图像干扰检测

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014617

Haoyu Zhang, Sinong Quan, Shiqi Xing, Yitao Liu

{"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}

引用次数: 0

Research on Green glass of Cantonese colored windows based on color model 基于色彩模型的广东彩窗绿色玻璃研究

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014495

Xiaoqing Wang, Ying Du

引用次数: 0

The automated segmentation and enhancement of cracks on airport pavements using three-dimensional imaging techniques 利用三维成像技术自动分割和强化机场路面裂缝

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014473

Shanshan Zhai, Yanna Xu

引用次数: 0

A deep learning approach for fruit detection: YOLO-GF 水果检测的深度学习方法YOLO-GF

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014430

J. Guo, Wei Wu

引用次数: 0

The application of target tracking algorithm in intelligent video system to flight support 智能视频系统中目标跟踪算法在飞行支持中的应用

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014375

Jianjun Peng, Jialei Zhai, Xiang Jin, Chengshuang Hu, Zaigang Li

{"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}

引用次数: 0

Research on target detection algorithm based on vehicle detection 基于车辆检测的目标检测算法研究

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014382

Yanguo Huang, Zehao Rao, Luo Li

引用次数: 0

Optimization research on pedestrian multiobjects tracking model based on TBD strategy 基于 TBD 策略的行人多目标跟踪模型优化研究

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014360

Shi Wang, Xiangju Liu, Xinshu Liu, JiaHui Chen, XiaoHong Wang

{"title":"Optimization research on pedestrian multiobjects tracking model based on TBD strategy","authors":"Shi Wang, Xiangju Liu, Xinshu Liu, JiaHui Chen, XiaoHong Wang","doi":"10.1117/12.3014360","DOIUrl":"https://doi.org/10.1117/12.3014360","url":null,"abstract":"The main task of pedestrian multi objects tracking technology is to continuously track multiple pedestrian objects simultaneously in video sequences and maintain their unique ID numbers. However, current pedestrian multi objects tracking models still have many problems, such as false detection, missed detection, and frequent ID number switching when pedestrians are obstructed or have overly similar appearances, ultimately leading to tracking failure. Therefore, this paper proposes a pedestrian multi objects tracking model based on TBD strategy. It mainly consists of two parts: pedestrian detector and pedestrian tracker. In terms of pedestrian detectors, this paper uses ES-YOLO pedestrian detectors. In terms of pedestrian trackers, this paper draws on the Omni-scale feature learning module in OSNet to redesign the StrongSORT pedestrian appearance feature extraction network, and ultimately obtains the StrongSORT pedestrian tracker based on omni-scale feature fusion, further enhancing its pedestrian feature extraction ability. In terms of experimental results. The experimental results of the pedestrian multi objects tracking model based on the TBD strategy in this paper on the MOT16 dataset show that the proposed pedestrian multi-objective tracking model can effectively improve the accuracy of pedestrian multi objects tracking and reduce the problem of frequent pedestrian ID number switching.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":"33 4","pages":"129692K - 129692K-7"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140512240","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}

引用次数: 0

Research on the simplification of building complex model under multi-factor constraints 多因素约束下建筑复杂模型的简化研究

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014388

Haoyuan Bai, Kelong Yang, Shunhua Liao

引用次数: 0

RGB-D visual SLAM for point association local edge features 针对点关联局部边缘特征的 RGB-D 视觉 SLAM

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014358

Hongtu Li, Fang Wang, Yunjiang Zhang

引用次数: 0

Identification of customer electricity usage anomalies based on random matrix theory 基于随机矩阵理论识别用户用电异常情况

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014405

Shuo Zhou, Qihui Wang

引用次数: 0