{"title":"基于边缘特征的航空图像运动目标检测:比较研究","authors":"Z. R. Mahayuddin, A. Saif","doi":"10.1109/ICCED51276.2020.9415786","DOIUrl":null,"url":null,"abstract":"Edge features or characteristics are the significant local intensity variation of pictorial structure in an image or frame or video sequence. Edge detection process finds the existence and position of pixels causes by significant differences in intensity of the images or frames or video sequences. However, selection of appropriate edge features specially to detect moving objects using aerial types of video sequences is still an elusive research concern because of various constraints for aerial frames, i.e. various altitudes, lack of features available for detection, motion variation etc. This research proposes comparative evaluation of different edge features based moving object detection methods to make it easy to decide which edge features based detection method is appropriate for extraction of moving object from aerial frames. Proposed research selected three edge features for detection, i.e. Sobel, Prewitt and Canny for moving object extraction to process high and low pictorial brightness variation for aerial video sequences. Proposed research illustrates the comparative performance of edge features based moving object detection. Each of the method is evaluated with extensive experimental analysis with standard performance metrics. Experimental results demonstrate effective comparison among each method with various performance metrics to ensure optimum detection performance for moving objects.","PeriodicalId":344981,"journal":{"name":"2020 6th International Conference on Computing Engineering and Design (ICCED)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Edge Feature based Moving Object Detection Using Aerial Images: A Comparative Study\",\"authors\":\"Z. R. Mahayuddin, A. Saif\",\"doi\":\"10.1109/ICCED51276.2020.9415786\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Edge features or characteristics are the significant local intensity variation of pictorial structure in an image or frame or video sequence. Edge detection process finds the existence and position of pixels causes by significant differences in intensity of the images or frames or video sequences. However, selection of appropriate edge features specially to detect moving objects using aerial types of video sequences is still an elusive research concern because of various constraints for aerial frames, i.e. various altitudes, lack of features available for detection, motion variation etc. This research proposes comparative evaluation of different edge features based moving object detection methods to make it easy to decide which edge features based detection method is appropriate for extraction of moving object from aerial frames. Proposed research selected three edge features for detection, i.e. Sobel, Prewitt and Canny for moving object extraction to process high and low pictorial brightness variation for aerial video sequences. Proposed research illustrates the comparative performance of edge features based moving object detection. Each of the method is evaluated with extensive experimental analysis with standard performance metrics. Experimental results demonstrate effective comparison among each method with various performance metrics to ensure optimum detection performance for moving objects.\",\"PeriodicalId\":344981,\"journal\":{\"name\":\"2020 6th International Conference on Computing Engineering and Design (ICCED)\",\"volume\":\"100 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 6th International Conference on Computing Engineering and Design (ICCED)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCED51276.2020.9415786\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 6th International Conference on Computing Engineering and Design (ICCED)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCED51276.2020.9415786","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Edge Feature based Moving Object Detection Using Aerial Images: A Comparative Study
Edge features or characteristics are the significant local intensity variation of pictorial structure in an image or frame or video sequence. Edge detection process finds the existence and position of pixels causes by significant differences in intensity of the images or frames or video sequences. However, selection of appropriate edge features specially to detect moving objects using aerial types of video sequences is still an elusive research concern because of various constraints for aerial frames, i.e. various altitudes, lack of features available for detection, motion variation etc. This research proposes comparative evaluation of different edge features based moving object detection methods to make it easy to decide which edge features based detection method is appropriate for extraction of moving object from aerial frames. Proposed research selected three edge features for detection, i.e. Sobel, Prewitt and Canny for moving object extraction to process high and low pictorial brightness variation for aerial video sequences. Proposed research illustrates the comparative performance of edge features based moving object detection. Each of the method is evaluated with extensive experimental analysis with standard performance metrics. Experimental results demonstrate effective comparison among each method with various performance metrics to ensure optimum detection performance for moving objects.