2023 4th International Conference on Advancements in Computational Sciences (ICACS)最新文献

筛选

英文中文

Highway Traffic Surveillance Over UAV Dataset via Blob Detection and Histogram of Gradient 基于Blob检测和梯度直方图的无人机数据集公路交通监控

2023 4th International Conference on Advancements in Computational Sciences (ICACS) Pub Date : 2023-02-20 DOI: 10.1109/ICACS55311.2023.10089709

Asifa Mehmood Qureshi, Abdul Haleem Butt, A. Jalal

{"title":"Highway Traffic Surveillance Over UAV Dataset via Blob Detection and Histogram of Gradient","authors":"Asifa Mehmood Qureshi, Abdul Haleem Butt, A. Jalal","doi":"10.1109/ICACS55311.2023.10089709","DOIUrl":"https://doi.org/10.1109/ICACS55311.2023.10089709","url":null,"abstract":"Systems for traffic monitoring often rely on local platforms. Aerial images provide the flexibility to sense the vehicle location and movements with appropriate resolution over a broader area via mobile platforms. This research study presents a method to monitor traffic flow effectively using aerial images. A novel dataset was used to evaluate the research study. In total 100 images were used, and further each image was divided into a burst of three images. The detection is being done on the first image of each burst whereas the other two images were used for tracking. Vehicle detection mainly depends on blob detection techniques with dynamic thresholding methods. To track vehicles, a shape model is generated for each of the detected vehicles. The model is used for template matching to locate all possible positions of the vehicle. The tracking results are improved by selecting only those matches having the direction and magnitude above a threshold. The accuracy of the detection algorithm in terms of correctness and completeness is 86% and 79% respectively. Through geometric computation, 73.4% of vehicles' shape model was correctly estimated. Further, the estimated shape models were used for tracking which tracked 74.9% of vehicles correctly.","PeriodicalId":357522,"journal":{"name":"2023 4th International Conference on Advancements in Computational Sciences (ICACS)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114251383","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: 10

Object Detection and Segmentation for Scene Understanding via Random Forest 基于随机森林的场景理解中的目标检测和分割

2023 4th International Conference on Advancements in Computational Sciences (ICACS) Pub Date : 2023-02-20 DOI: 10.1109/ICACS55311.2023.10089658

Bisma Riaz Chughtai, A. Jalal

{"title":"Object Detection and Segmentation for Scene Understanding via Random Forest","authors":"Bisma Riaz Chughtai, A. Jalal","doi":"10.1109/ICACS55311.2023.10089658","DOIUrl":"https://doi.org/10.1109/ICACS55311.2023.10089658","url":null,"abstract":"In recent days, object detection become a vast topic in computer vision. Accurate object detection and scene understanding is not an easy task due to illumination, viewpoints, and color intensities. Visual features like color, texture, boundaries, and shape, make an image different from another image. The main goal of scene understanding is to machine work like a human and understand the visual information of an image. Currently, researchers working on novel approaches in this field to make a better understanding of the scene. Computer vision portrays a major role in different applications such as health, safety, security surveillance, traffic monitoring, autonomous driving car, object recognition, and tracking. In this paper, we work on a meaningful understanding of an image in the scene. To understand the scene we have done region-based segmentation, and for object detection and labeling, we use the tensor flow algorithm, for geometric features mean of each pixel, harry corner edge detection, and scale-invariant feature transform descriptor. And then object recognition by using random forest. We have performed this experiment on UIUC Sports dataset. The presented model achieved 89.45% recognition accuracy.","PeriodicalId":357522,"journal":{"name":"2023 4th International Conference on Advancements in Computational Sciences (ICACS)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125022849","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: 1

首页上一页