Fadwa Benjelloun, Imane El Manaa, M. A. Sabri, Ali Yahyaouy, A. Aarab
{"title":"快速Yolo模型和Delaunay三角剖分法两种目标检测方法的比较","authors":"Fadwa Benjelloun, Imane El Manaa, M. A. Sabri, Ali Yahyaouy, A. Aarab","doi":"10.1109/ISCV49265.2020.9204197","DOIUrl":null,"url":null,"abstract":"Image segmentation, object detection and classification are three closely related tasks that can be greatly improved when they are jointly solved by feeding information from one task to another. Different methods have been proposed by the researchers, some of which have given good results and others fail in certain circumstances. In our paper, we compare two techniques for recognizing moving objects in a video scene. The first approach is based on deep learning. We implemented the Fast Yolo model to detect objects. The second approach is based on the segmentation of objects, we used the Delaunay Triangulation method to recover homogeneous regions. We have combined the features of the HOG, color histogram, and GLCM associated with each object. The classification phase is carried out by Alexnet for both approaches. The experiment was carried out on several video clips of highways and local roads with different traffic and lighting conditions.","PeriodicalId":313743,"journal":{"name":"2020 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"The comparison between two methods of object detection: Fast Yolo model and Delaunay Triangulation\",\"authors\":\"Fadwa Benjelloun, Imane El Manaa, M. A. Sabri, Ali Yahyaouy, A. Aarab\",\"doi\":\"10.1109/ISCV49265.2020.9204197\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Image segmentation, object detection and classification are three closely related tasks that can be greatly improved when they are jointly solved by feeding information from one task to another. Different methods have been proposed by the researchers, some of which have given good results and others fail in certain circumstances. In our paper, we compare two techniques for recognizing moving objects in a video scene. The first approach is based on deep learning. We implemented the Fast Yolo model to detect objects. The second approach is based on the segmentation of objects, we used the Delaunay Triangulation method to recover homogeneous regions. We have combined the features of the HOG, color histogram, and GLCM associated with each object. The classification phase is carried out by Alexnet for both approaches. The experiment was carried out on several video clips of highways and local roads with different traffic and lighting conditions.\",\"PeriodicalId\":313743,\"journal\":{\"name\":\"2020 International Conference on Intelligent Systems and Computer Vision (ISCV)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 International Conference on Intelligent Systems and Computer Vision (ISCV)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCV49265.2020.9204197\",\"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 International Conference on Intelligent Systems and Computer Vision (ISCV)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCV49265.2020.9204197","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The comparison between two methods of object detection: Fast Yolo model and Delaunay Triangulation
Image segmentation, object detection and classification are three closely related tasks that can be greatly improved when they are jointly solved by feeding information from one task to another. Different methods have been proposed by the researchers, some of which have given good results and others fail in certain circumstances. In our paper, we compare two techniques for recognizing moving objects in a video scene. The first approach is based on deep learning. We implemented the Fast Yolo model to detect objects. The second approach is based on the segmentation of objects, we used the Delaunay Triangulation method to recover homogeneous regions. We have combined the features of the HOG, color histogram, and GLCM associated with each object. The classification phase is carried out by Alexnet for both approaches. The experiment was carried out on several video clips of highways and local roads with different traffic and lighting conditions.